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1

YU, SHENGSHENG, CHAOBING HUANG, and JINGLI ZHOU. "COLOR IMAGE RETRIEVAL BASED ON COLOR-TEXTURE-EDGE FEATURE HISTOGRAMS." International Journal of Image and Graphics 06, no. 04 (2006): 583–98. http://dx.doi.org/10.1142/s0219467806002392.

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In this paper, two novel texture descriptors and two novel edge descriptors are proposed, which are low-dimension, effective, and are obtained by a relative simple approach. The two texture descriptors are the directional difference unit and the gradient unit histogram, which are rotation invariant. The two edge descriptors are the quantized max-min difference histogram and the quantized fuzzy entropy histogram, the former is more suitable for describing the images with relatively regular texture characteristic, the latter is more suitable for describing the images with relatively regular structure characteristic or no regular characteristic. Combing color descriptor, texture descriptor and edge descriptor to form hybrid visual feature index to retrieve natural color images, this method is insensitive to image rotation and translation. Experimental results show that the method achieves better performance than other recent relevant methods.
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Li, Zeyi, Haitao Zhang, and Yihang Huang. "A Rotation-Invariant Optical and SAR Image Registration Algorithm Based on Deep and Gaussian Features." Remote Sensing 13, no. 13 (2021): 2628. http://dx.doi.org/10.3390/rs13132628.

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Traditional feature matching methods of optical and synthetic aperture radar (SAR) used gradient are sensitive to non-linear radiation distortions (NRD) and the rotation between two images. To address this problem, this study presents a novel approach to solving the rigid body rotation problem by a two-step process. The first step proposes a deep learning neural network named RotNET to predict the rotation relationship between two images. The second step uses a local feature descriptor based on the Gaussian pyramid named Gaussian pyramid features of oriented gradients (GPOG) to match two images. The RotNET uses a neural network to analyze the gradient histogram of the two images to derive the rotation relationship between optical and SAR images. Subsequently, GPOG is depicted a keypoint by using the histogram of Gaussian pyramid to make one-cell block structure which is simpler and more stable than HOG structure-based descriptors. Finally, this paper designs experiments to prove that the gradient histogram of the optical and SAR images can reflect the rotation relationship and the RotNET can correctly predict them. The similarity map test and the image registration results obtained on experiments show that GPOG descriptor is robust to SAR speckle noise and NRD.
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Zeng, Hui, Rui Zhang, Mingming Huang, and Xiuqing Wang. "Compact Local Directional Texture Pattern for Local Image Description." Advances in Multimedia 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/360186.

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This paper presents an effective local image feature region descriptor, called CLDTP descriptor (Compact Local Directional Texture Pattern), and its application in image matching and object recognition. The CLDTP descriptor encodes the directional and contrast information in a local region, so it contains the gradient orientation information and the gradient magnitude information. As the dimension of the CLDTP histogram is much lower than the dimension of the LDTP histogram, the CLDTP descriptor has higher computational efficiency and it is suitable for image matching. Extensive experiments have validated the effectiveness of the designed CLDTP descriptor.
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Yang, Jianguo, Dian Sheng, Weiqi Jin, and Li Li. "Histogram of Polarization Gradient for Target Tracking in Infrared DoFP Polarization Thermal Imaging." Remote Sensing 17, no. 5 (2025): 907. https://doi.org/10.3390/rs17050907.

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Division-of-focal-plane (DoFP) polarization imaging systems have demonstrated considerable promise in target detection and tracking in complex backgrounds. However, existing methods face challenges, including dependence on complex image preprocessing procedures and limited real-time performance. To address these issues, this study presents a novel histogram of polarization gradient (HPG) feature descriptor that enables efficient feature representation of polarization mosaic images. First, a polarization distance calculation model based on normalized cross-correlation (NCC) and local variance is constructed, which enhances the robustness of gradient feature extraction through dynamic weight adjustment. Second, a sparse Laplacian filter is introduced to achieve refined gradient feature representation. Subsequently, adaptive polarization channel correlation weights and the second-order gradient are utilized to reconstruct the degree of linear polarization (DoLP). Finally, the gradient and DoLP sign information are ingeniously integrated to enhance the capability of directional expression, thus providing a new theoretical perspective for polarization mosaic image structure analysis. The experimental results obtained using a self-developed long-wave infrared DoFP polarization thermal imaging system demonstrate that, within the same FBACF tracking framework, the proposed HPG feature descriptor significantly outperforms traditional grayscale {8.22%, 2.93%}, histogram of oriented gradient (HOG) {5.86%, 2.41%}, and mosaic gradient histogram (MGH) {27.19%, 18.11%} feature descriptors in terms of precision and success rate. The processing speed of approximately 20 fps meets the requirements for real-time tracking applications, providing a novel technical solution for polarization imaging applications.
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Ouanan, Hamid, Mohammed Ouanan, and Brahim Aksasse. "Gabor-HOG Features based Face Recognition Scheme." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 331. http://dx.doi.org/10.11591/tijee.v15i2.1546.

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Extraction of invariant features is the core of Face RecognitionSystems (FRS). This work proposes a novel feature extractor-fusion scheme using two powerful feature descriptor known as Gabor Filters (GFs) and Histogram of Oriented Gradient (HOG), which the face image is filtered with the multiscale multiresolution Gabor filter bank to generate multiple Gabor magnitude images (GMIs), then the down-sampled GMIs and apply Histogram of Oriented Gradient to form the features. The experimental results on the FERET face database show the effectiveness of our methods.
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Alreshidi, Eissa, Rabie A. Ramadan, Md Haidar Sharif, Omer Faruk Ince, and Ibrahim Furkan Ince. "A Comparative Study of Image Descriptors in Recognizing Human Faces Supported by Distributed Platforms." Electronics 10, no. 8 (2021): 915. http://dx.doi.org/10.3390/electronics10080915.

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Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images.
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Patel, Chirag I., Dileep Labana, Sharnil Pandya, Kirit Modi, Hemant Ghayvat, and Muhammad Awais. "Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences." Sensors 20, no. 24 (2020): 7299. http://dx.doi.org/10.3390/s20247299.

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Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors.
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Li, Bin, Kaili Cheng, and Zhezhou Yu. "Histogram of Oriented Gradient Based Gist Feature for Building Recognition." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6749325.

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We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.
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9

Tong, Ying, Liang Bao Jiao, and Xue Hong Cao. "A Novel HOG Descriptor with Spatial Multi-Scale Feature for FER." Applied Mechanics and Materials 596 (July 2014): 322–27. http://dx.doi.org/10.4028/www.scientific.net/amm.596.322.

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HOG Feature is an efficient edge information descriptor, but it ignores the spatial arrangement of local FER features. In this respect, this paper puts forward a spatial multi-scale model based on an improved HOG algorithm which uses canny operator instead of traditional gradient operator. After the image is divided into a series of sub-regions layer by layer, the histogram of orient gradients for each sub-region is calculated and connected in sequence to obtain the spatial multi-scale HOG feature of whole image. Compared with traditional HOG and the improved PHOG, the proposed SMS_HOG algorithm acquires 5% recognition rate improvement and 50% processing time reduction.
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Yu, Yang, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, and Jiayi Ma. "A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching." Remote Sensing 13, no. 23 (2021): 4912. http://dx.doi.org/10.3390/rs13234912.

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Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.
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Jiao, Jichao, and Zhongliang Deng. "Deep combining of local phase quantization and histogram of oriented gradients for indoor positioning based on smartphone camera." International Journal of Distributed Sensor Networks 13, no. 1 (2017): 155014771668697. http://dx.doi.org/10.1177/1550147716686978.

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To achieve high accuracy in indoor positioning using a smartphone, there are two limitations: (1) limited computational and memory resources of the smartphone and (2) the human walking in large buildings. To address these issues, we propose a new feature descriptor by deeply combining histogram of oriented gradients and local phase quantization. This feature is a local phase quantization of a salient histogram of oriented gradient visualizing image, which is robust in indoor scenarios. Moreover, we introduce a base station–based indoor positioning system for assisting to reduce the image matching at runtime. The experimental results show that accurate and efficient indoor location positioning is achieved.
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12

Chu, Kai, and Guang-Hai Liu. "Image Retrieval Based on a Multi-Integration Features Model." Mathematical Problems in Engineering 2020 (March 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/1461459.

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Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.
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13

Kolawole, Adeola O., Martins E. Irhebhude, and Philip O. Odion. "Human Action Recognition in Military Obstacle Crossing Using HOG and Region-Based Descriptors." Journal of Computing Theories and Applications 2, no. 3 (2025): 410–26. https://doi.org/10.62411/jcta.12195.

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Human action recognition involves recognizing and classifying actions performed by humans. It has many applications, including sports, healthcare, and surveillance. Challenges such as a limited number of classes of activities and variations within inter and intra-class groups lead to high misclassification rates in some of the intelligent systems developed. Existing studies focused mainly on using public datasets with little focus on real-life action datasets, with limited research on HAR for military obstacle-crossing activities. This paper focuses on recognizing human actions in an obstacle-crossing competition video sequence where multiple participants are performing different obstacle-crossing activities. This study proposes a feature descriptor approach that combines a Histogram of Oriented Gradient and Region Descriptors (HOGReG) for human action recognition in a military obstacle crossing competition. The dataset was captured during military trainees’ obstacle-crossing exercises at a military training institution to achieve this objective. Images were segmented into background and foreground using a Grabcut-based segmentation algorithm, and thereafter, features were extracted and used for classification. The features were extracted using a Histogram of Oriented Gradient (HOG) and region descriptors from segmented images. The extracted features are presented to a neural network classifier for classification and evaluation. The experimental results recorded 63.8%, 82.6%, and 86.4% recognition accuracies using the region descriptors HOG and HOGReG, respectively. The region descriptor gave a training time of 5.6048 seconds, while HOG and HOGReG reported 32.233 and 31.975 seconds, respectively. The outcome shows how effectively the suggested model performed.
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Zhang, Jianguang, Yongxia Li, An Tai, Xianbin Wen, and Jianmin Jiang. "Motion Video Recognition in Speeded-Up Robust Features Tracking." Electronics 11, no. 18 (2022): 2959. http://dx.doi.org/10.3390/electronics11182959.

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Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is used to detect the candidate points of the video because it is an efficient faster local feature detector. Secondly, by using the optical flow field and trajectory, the feature points can be filtered from the candidate points, which enables a robust and efficient extraction of motion feature points. Additionally, we introduce a descriptor, called MoSURF (Motion Speeded-Up Robust Features), based on SURF (Speeded-Up Robust Features), HOG (Histogram of Oriented Gradient), HOF (Histograms of Optical Flow), MBH(Motion Boundary Histograms), and trajectory information, which can effectively describe motion information and are complementary to each other. We evaluate our video representation under action classification on three motion video datasets namely KTH, YouTube, and UCF50. Compared with state-of-the-art methods, the proposed method shows advanced results on all datasets.
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Lin, Bo, and Bin Fang. "A new spatial-temporal histograms of gradients descriptor and HOD-VLAD encoding for human action recognition." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (2019): 1940009. http://dx.doi.org/10.1142/s0219691319400095.

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Automatic human action recognition is a core functionality of systems for video surveillance and human object interaction. In the whole recognition system, feature description and encoding represent two crucial key steps. In order to construct a powerful action recognition framework, it is important that the two steps must provide reliable performance. In this paper, we proposed a new human action feature descriptor which is called spatio-temporal histograms of gradients (SPHOG). SPHOG is based on the spatial and temporal derivation signal, which extracts the gradient changes between consecutive frames. Compared to the traditional descriptors histograms of optical flow, our proposed SPHOG costs less computation resource. In order to incorporate the distribution information of local descriptors into Vector of Locally Aggregated Descriptors (VLAD), which is a popular encoding approach for Bag-of-Feature representation, a Gaussian kernel is implanted to compute the weighted distance histograms of local descriptors. By doing this, the encoding schema for bag-of-feature (BOF) representation is more effective. We validated our proposed algorithm for human action recognition on three public available datasets KTH, UCF Sports and HMDB51. The evaluation experiment results indicate that the proposed descriptor and encoding method can improve the efficiency of human action recognition and the recognition accuracy.
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Susanto, Ajib, Christy Atika Sari, Ibnu Utomo Wahyu Mulyono, and Mohamed Doheir. "Histogram of Gradient in K-Nearest Neighbor for Javanese Alphabet Classification." Scientific Journal of Informatics 8, no. 2 (2021): 289–96. http://dx.doi.org/10.15294/sji.v8i2.30788.

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Purpose: The Javanese script generally has a basic script or is commonly referred to as the “carakan” script. The script consists of 20 letters with different levels of difficulty. Some letters have similarities, so research is needed to make it easier to detect the image of Javanese characters. Methods: This study proposes recognizing Hiragana's writing characters using the K-Nearest Neighbor (K-NN) method. In the preprocessing stage, the segmentation process is carried out using the thresholding method to perform segmentation, followed by the Histogram of Gradient (HOG) feature extraction process and noise removal using median filtering. Histogram of Gradient (HoG) is one of the features used in computer vision and image processing in detecting an object in the form of a descriptor feature. There are 1000 data divided into 20 classes. Each class represents one letter of the basic Javanese script. Result: Based on data collection using the writings of 50 respondents where each respondent writes 20 basic Javanese characters, the highest accuracy was obtained at K = 1, namely 98.5%. Novelty: Using several preprocessing such as cropping, median filtering, otsu thresholding and HOG feature extraction before do classification, this experiment yields a good accuracy.
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Ayeche, Farid, and Adel Alti. "Novel Descriptors for Effective Recognition of Face and Facial Expressions." Revue d'Intelligence Artificielle 34, no. 5 (2020): 521–30. http://dx.doi.org/10.18280/ria.340501.

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In this paper, we present a face recognition approach based on extended Histogram Oriented Gradient (HOG) descriptors to extract the facial expressions features allowing classifying the faces and facial expressions. The approach is based on determining the different directional codes on the face image based on edge response values to define the feature vector from the face image. Its size is reduced to improve the performance of the SVM (Support Vector Machine) classifier. Experiments are conducted using two public datasets: JAFFE for facial expression recognition and YALE for face recognition. Experimental results show that the proposed descriptor achieves recognition rate of 92.12% and execution time ranging from 0.4s to 0.7s in all evaluated databases compared with existing works. Experiments demonstrate and confirm both the effectiveness and the efficiency of the proposed descriptor.
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Umar, Siyudi Shafi’I, Zaharaddeen S. Iro, Abubakar Y. Zandam, and Saifulllahi Sadi Shitu. "Accelerated Histogram of Oriented Gradients for Human Detection." Dutse Journal of Pure and Applied Sciences 9, no. 1a (2023): 44–56. http://dx.doi.org/10.4314/dujopas.v9i1a.5.

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Histogram of Oriented Gradients (HOG) is an object detection algorithm used to detect people from an image. It involves features extraction called ‘HOG descriptor’ which are used to identify a person in the image. Several operations are involved in the feature extraction process. Hence performing numerous computations in order to obtain HOG descriptors takes some considerable amount of time. This slow computation speed limits HOG’s application in real-time systems. This paper investigates HOG with a view to improve its speed, modify the feature computation process to develop a faster version of HOG and finally evaluate against existing HOG. The technique of asymptotic notation in particular Big-O notation was applied to each stage of HOG and the complexity for the binning stage was modified. This results in a HOG version with a reduced complexity from n4 to n2 thereby having an improved speed as compared to the original HOG.
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Thirthe Gowda, M. T., and J. Chandrika. "Optimized Scale-Invariant Hog Descriptors for Tobacco Plant Detection." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 17 (July 23, 2021): 787–94. http://dx.doi.org/10.37394/232015.2021.17.74.

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The histogram of gradient (HOG) descriptor is being employed in this research work to demonstrate the technique of scale variant to identify the plant in surveillance videos. In few scenarios, the discrepancies in the histogram of gradient descriptors along with scale as well as variation in illumination are considered as one of the major hindrances. This research work introduces a unique SIO-HOG descriptor that is approximated to be scale-invariant. With the help of the footage that is captured from the tobacco plant identification process, the system can integrate adoptive bin selections as well as sample resizing. Further, this research work explores the impact of a PCA transform that is based on the process of feature selection on the performance of overall recognition and thereby considering finite scale range, adoptive orientation binning in non-overlapping descriptors, as well as finite scale range are all essential for a high detection rate. The feature vector of HOG over a complete search window is computationally intensive. However, suitable frameworks for classification can be developed by maintaining a precise range of attributes with finite Euclidean distance. Experimental results prove that the proposed approach for detecting tobacco from other weeds has resulted in an improved detection rate. And finally, the robustness of the complete plant detection system was evaluated on a video sequence with different non-linearity's that is quite common in a real-world environment and its performance metrics are evaluated
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WEN, JING, BIN FANG, Y. Y. TANG, PATRICK S. P. WANG, MIAO CHENG, and TAIPING ZHANG. "COMBINING EODH AND DIRECTIONAL GRADIENT DENSITY FOR OFFLINE SIGNATURE VERIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 06 (2009): 1161–77. http://dx.doi.org/10.1142/s0218001409007491.

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The main problem to identify skilled forgeries for offline signature verification lies in the fact that it is difficult to formalize distinguished feature representation of the signature patterns and design appropriate fusion scheme for various types of feature vectors. To tackle these problems, in this paper, we propose an approach to extract robust Edge Orientation Distance Histogram (EODH) descriptor which effectively reflects signature structure variations. In addition, directional gradient density features are employed for skilled forgery verification attempt. To exploit the full capacity of two sets of features, we designed the multilevel weighted fuzzy classifier and fuse match scores by way of selection priority. Experiments were conducted on a subcorpus of open MCYT signature database which is widely used for performance evaluation. It shows that the proposed method was able to improve verification accuracy.
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Liu, Xiao Lei. "Dance Movement Recognition Based on Multimodal Environmental Monitoring Data." Journal of Environmental and Public Health 2022 (July 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/1568930.

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Fine motion recognition is a challenging topic in computer vision, and it has been a trendy research direction in recent years. This study combines motion recognition technology with dance movements and the problems such as the high complexity of dance movements and fully considers the human body’s self-occlusion. The excellent motion recognition content in the dance field was studied and analyzed. A compelling feature extraction method was proposed for the dance video dataset, segmented video, and accumulated edge feature operation. By extracting directional gradient histogram features, a set of directional gradient histogram feature vectors is used to characterize the shape features of the dance video movements. A dance movement recognition method is adopted based on the fusion direction gradient histogram feature, optical flow direction histogram feature, and audio signature feature. Three components are combined for dance movement recognition by a multicore learning method. Experimental results show that the cumulative edge feature algorithm proposed in this study outperforms traditional models in the recognition results of HOG features extracted from images. After adding edge features, the description of the dance movement shape is more effective. The algorithm can guarantee a specific recognition rate of complex dance movements. The results also verify the effectiveness of the movement recognition algorithm in this study for dance movement recognition.
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Wang, Yan, Ming Li, Xing Wan, Congxuan Zhang, and Yue Wang. "Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition." Computational Intelligence and Neuroscience 2020 (December 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/8886872.

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Obtaining a valid facial expression recognition (FER) method is still a research hotspot in the artificial intelligence field. In this paper, we propose a multiparameter fusion feature space and decision voting-based classification for facial expression recognition. First, the parameter of the fusion feature space is determined according to the cross-validation recognition accuracy of the Multiscale Block Local Binary Pattern Uniform Histogram (MB-LBPUH) descriptor filtering over the training samples. According to the parameters, we build various fusion feature spaces by employing multiclass linear discriminant analysis (LDA). In these spaces, fusion features composed of MB-LBPUH and Histogram of Oriented Gradient (HOG) features are used to represent different facial expressions. Finally, to resolve the inconvenient classifiable pattern problem caused by similar expression classes, a nearest neighbor-based decision voting strategy is designed to predict the classification results. In experiments with the JAFFE, CK+, and TFEID datasets, the proposed model clearly outperformed existing algorithms.
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Athira, TR, and Varghese Abraham. "CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 8–17. https://doi.org/10.5281/zenodo.4108200.

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Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.
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Bindu, Hima, and Manjunathachari K. "Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition." Sensor Review 38, no. 3 (2018): 269–81. http://dx.doi.org/10.1108/sr-06-2017-0115.

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Purpose This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend. Design/methodology/approach This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database. Findings The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01. Originality/value This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.
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Giveki, Davar, Mohammad Ali Soltanshahi, and Gholam Ali Montazer. "A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern." Optik - International Journal for Light and Electron Optics 131 (June 7, 2016): 242–54. https://doi.org/10.5281/zenodo.13998082.

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This paper presents a new methodology to retrieve images of different scenes by introducing a novel image descriptor.‎ The proposed descriptor works with Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local Derivative Pattern (LDP), Local Ternary Pattern (LTP) and any other feature descriptor that can be applied on the image pixels.‎ As the proposed descriptor considers a group of pixels together, higher level of semantic is achieved.‎ In this work, a new image descriptor using SIFT and LDP is introduced that is able to find similarities and matches between images.‎ The proposed descriptor produces highly discriminative features for describing image content.‎ Four image datasets are used for evaluating our proposed descriptor.‎ Comprehensive experiments have been conducted using various classifiers and different image features to show the superiority of the proposed method.‎
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Alzahrani, Abdullah. "Efficient Descriptor of Histogram of Ridges Orientation Delineate for Fingernail." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (2023): 34–42. http://dx.doi.org/10.35940/ijrte.a7577.0912323.

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Fingernails structure are rich in orientation, ridges and edge features. Inspired by Edge Histogram Descriptor (EHD), this paper presents an efficient orientation-based local descriptor, named histogram of ridges orientation delineate (HROD). HROD is based on the fact that human vision is sensitive to edge features for image perception. For a given image, HROD algorithm first execute and perform a pre-process i.e., re-sizing, filtering, enhancement, segmentation, edge detection and feature extraction. Then, finds oriented edge maps according to predefined orientations using a well-known edge operator mask (2×2 sub block) and obtains a ridges orientation delineate map by choosing an orientation with the maximum edge magnitude for each pixel. In the experiment on this research, five oriented edge maps were used to generate and detect the maximum edge orientation construction of each block, namely vertical, horizontal, diagonal 45°, diagonal 135° and isotropic (non-orientation specific) orientation. Experimental results on fingernail images show that the performance of HROD comparable with the state-of-the-art orientation-based methods (e.g., Gabor filter, histogram of oriented gradients, and local directional code). Furthermore, the proposed HROD algorithm has advantages of low feature dimensionality and fast implementation for a real-time fingernails orientation recognition system.
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Abdullah, Alzahrani. "Efficient Descriptor of Histogram of Ridges Orientation Delineate for Fingernail." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (2023): 34–42. https://doi.org/10.35940/ijrte.A7577.0912323.

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Fingernails structure are rich in orientation, ridges and edge features. Inspired by Edge Histogram Descriptor (EHD), this paper presents an efficient orientation-based local descriptor, named histogram of ridges orientation delineate (HROD). HROD is based on the fact that human vision is sensitive to edge features for image perception. For a given image, HROD algorithm first execute and perform a pre-process i.e., re-sizing, filtering, enhancement, segmentation, edge detection and feature extraction. Then, finds oriented edge maps according to predefined orientations using a well-known edge operator mask (2×2 sub block) and obtains a ridges orientation delineate map by choosing an orientation with the maximum edge magnitude for each pixel. In the experiment on this research, five oriented edge maps were used to generate and detect the maximum edge orientation construction of each block, namely vertical, horizontal, diagonal 45°, diagonal 135° and isotropic (non-orientation specific) orientation. Experimental results on fingernail images show that the performance of HROD comparable with the state-of-the-art orientation-based methods (e.g., Gabor filter, histogram of oriented gradients, and local directional code). Furthermore, the proposed HROD algorithm has advantages of low feature dimensionality and fast implementation for a real-time fingernails orientation recognition system.
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TR, Athira, and Abraham Varghese. "CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 8. http://dx.doi.org/10.11591/ijai.v6.i1.pp8-17.

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Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.
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Reddy, A. Mallikarjuna, V. Venkata Krishna, and L. Sumalatha. "Face recognition based on stable uniform patterns." International Journal of Engineering & Technology 7, no. 2 (2018): 626. http://dx.doi.org/10.14419/ijet.v7i2.9922.

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Face recognition (FR) is one of the challenging and active research fields of image processing, computer vision and biometrics with numerous proposed systems. We present a feature extraction method named “stable uniform local pattern (SULP)”, a refined variant of ULBP operator, for robust face recognition. The SULP directly applied on gradient face images (in x and y directions) of a single image for capturing significant fundamental local texture patterns to build up a feature vector of a face image. Histogram sequences of SULP images of the two gradient images are finally concatenated to form the “stable uniform local pattern gradient (SULPG)” vector for the given image. The SULPG approach is experimented on Yale, ATT-ORL, FERET, CAS-PEAL and LFW face databases and the results are compared with the LBP model and various variants of LBP descriptor. The results indicate that the present descriptor is more powerful against a wide range of challenges, such as illumination, expression and pose variations and outperforms the state-of-the-art methods based on LBP.
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Mohanraj, V., V. Vaidehi, S. Vasuhi, and Ranajit Kumar. "A Novel Approach for Face Recognition under Varying Illumination Conditions." International Journal of Intelligent Information Technologies 14, no. 2 (2018): 22–42. http://dx.doi.org/10.4018/ijiit.2018040102.

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Face recognition systems are in great demand for domestic and commercial applications. A novel feature extraction approach is proposed based on TanTrigg Lower Edge Directional Patterns for robust face recognition. Histogram of Orientated Gradients is used to detect faces and the facial landmarks are localized using Ensemble of Regression Trees. The detected face is rotated based on facial landmarks using affine transformation followed by cropping and resizing. TanTrigg preprocessor is used to convert the aligned face region into an illumination invariant region for better feature extraction. Eight directional Kirsch compass masks are convolved with the preprocessed face image. Feature descriptor is extracted by dividing the TTLEDP image into several sub-regions and concatenating the histograms of all the sub-regions. Chi-square distance metric is used to match faces from the trained feature space. The experimental results prove that the proposed TTLEDP feature descriptor has better recognition rate than existing methods, overcoming the challenges like varying illumination and noise
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Mukherjee, Arnab, Md Zahidul Islam Islam, and Lasker Ershad Ali. "Human iris classification through Histogram of Oriented Gradient features with various distance metrics." Machine Graphics and Vision 33, no. 3/4 (2024): 97–124. https://doi.org/10.22630/mgv.2024.33.3.5.

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Human iris classification remains an active research area in the fields of biometrics as well as computer vision. In iris biometrics, most of the visible or near-infrared (NIR) eye images suffer from multiple noise sources, and the dispersive spectrum changes hugely. These changes occur due to spattering, albedo, and spectrum absorbance selectively. However, accurate iris classification for distance images is still a challenging task. To solve it effectively, we propose a machine learning (ML)-based iris classification employing a dense feature extraction method with various distance metrics. More specifically, this learning model focuses on the Histogram of Oriented Gradients (HOG) descriptor and K-Nearest Neighbour (K-NN) classifier with various distance metrics. The HOG descriptor has some advantages for this proposed distant-based iris classification, for example, insensitive to multiple lighting and noises, shift invariance, capacity to tolerate iris variations within the classes, etc. Additionally, this study investigates the most reliable distance metric that is less affected by different levels of noise. A publicly accessible CASIA-V4 distance image database is conducted for the experimental evaluation. To evaluate the performance of the classification models, we consider different measures such as recall, precision, F1-scores, and accuracy. The reported results are tabulated as well as optimized through Receiver Operating Characteristic (ROC) curves. The experimental results demonstrate that the Canberra distance metric with low dimensional HOG features provides better recognition accuracy (90.55%) compared to other distance metrics.
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Fawad, Muhammad Jamil Khan, MuhibUr Rahman, Yasar Amin, and Hannu Tenhunen. "Low-Rank Multi-Channel Features for Robust Visual Object Tracking." Symmetry 11, no. 9 (2019): 1155. http://dx.doi.org/10.3390/sym11091155.

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Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.
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Kailasam, S. Piramu, E. Siva Sankari, and R. Kumuthini. "ROC CURVE ANALYSIS OF DIFFERENT HYBRID FEATURE DESCRIPTORS USING MULTI CLASSIFIERS." ASEAN Engineering Journal 13, no. 2 (2023): 53–60. http://dx.doi.org/10.11113/aej.v13.18804.

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Tremendous success of machine learning algorithms at pattern recognition creates interest in new inventions. Machine learning in an era of big data is that significant hierarchical relationships within the data can be discovered algorithmically than other handcraft like features. In this study, Convolutional Neural Network (CNN) is used as feature descriptors in pulmonary malignancy prediction. Various feature descriptors such as Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradient (EXHOG) and Linear Binary Pattern (LBP) descriptors are analyzed with classifiers such as Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) for Computed Tomography (CT) The phenotype features of pulmonary nodules are important cues for identification. The nodule solidity is an important cue for white blob area identification. The method is analyzed in Lung Image Database Consortium (LIDC) dataset. Receivers Operating Characteristics (ROC) curves show the graphical summaries of detectors performance. It is proved that CNN based feature extraction with SVM classifier works well in pulmonary malignancy prediction.
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De Ocampo, Anton Louise Pernez, Argel Bandala, and Elmer Dadios. "Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring." International Journal of Advances in Intelligent Informatics 6, no. 3 (2020): 223. http://dx.doi.org/10.26555/ijain.v6i3.514.

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In UAV-based human detection, the extraction and selection of the feature vector are one of the critical tasks to ensure the optimal performance of the detection system. Although UAV cameras capture high-resolution images, human figures' relative size renders persons at very low resolution and contrast. Feature descriptors that can adequately discriminate between local symmetrical patterns in a low-contrast image may improve a human figures' detection in vegetative environments. Such a descriptor is proposed and presented in this paper. Initially, the acquired images are fed to a digital processor in a ground station where the human detection algorithm is performed. Part of the human detection algorithm is the GeHOG feature extraction, where a bank of Gabor filters is used to generate textured images from the original. The local energy for each cell of the Gabor images is calculated to identify the dominant orientations. The bins of conventional HOG are enhanced based on the dominant orientation index and the accumulated local energy in Gabor images. To measure the performance of the proposed features, Gabor-enhanced HOG (GeHOG) and other two recent improvements to HOG, Histogram of Edge Oriented Gradients (HEOG) and Improved HOG (ImHOG), are used for human detection on INRIA dataset and a custom dataset of farmers working in fields captured via unmanned aerial vehicle. The proposed feature descriptor significantly improved human detection and performed better than recent improvements in conventional HOG. Using GeHOG improved the precision of human detection to 98.23% in the INRIA dataset. The proposed feature can significantly improve human detection applied in surveillance systems, especially in vegetative environments.
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Benaissa, Manel, and Abdelhak Bennia. "New 2D Feature Descriptor Free from Orientation Compensation with k-Means Clustering." Journal of Advanced Engineering and Computation 2, no. 4 (2018): 251. http://dx.doi.org/10.25073/jaec.201824.211.

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In this paper, we propose two novel approaches in the field of feature description and matching. The first approach concerns the feature description and matching part, where we proposed an orientation invariant feature descriptor without an additional step dedicated to this task. We exploited the information provided by two representations of the image (intensity and gradient) for a better understanding and representation of the feature point distribution. The provided information is summarized in two cumulative histograms and used in the feature description and matching process. In the context of object detection, we introduced an unsupervised learning method based on k-means clustering. Which we used as an outlier pre-elimination phase after the matching process to improve our descriptor precision. Experiments shown its robustness to image changes and a clear increase in terms of precision of the tested descriptors after the pre-elimination phase.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
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Bakheet, Samy, and Ayoub Al-Hamadi. "A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification." Brain Sciences 11, no. 2 (2021): 240. http://dx.doi.org/10.3390/brainsci11020240.

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Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.
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Zhang, Li Hong. "Human Detection Based on SVM and Improved Histogram of Oriented Gradients." Applied Mechanics and Materials 380-384 (August 2013): 3862–65. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3862.

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Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.
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Sunil, S. Harakannanavar, Sapnakumari C, C. Ramachandra A, Pramodhini R, and R. Prashanth C. "Performance Evaluation of Fusion Based Efficient Algorithm for Facial Expression Recognition." Indian Journal of Science and Technology 16, no. 4 (2023): 266–76. https://doi.org/10.17485/IJST/v16i4.1891.

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ABSTRACT <strong>Objectives:</strong>&nbsp;To develop face expression recognition system using JAFFE database and to evaluate the performance of the face expression recognition models.&nbsp;<strong>Methods:</strong>&nbsp;This study used the FER model based on modified-HoG (Histogram of oriented gradient), LBP (Local Binary Patterns) and Fast Key point detector and BRIEF descriptor (FKBD) to extract the significant features of JAFFE dataset. The features extracted using HoG, LBP and FKBD techniques form a feature vector. Then, the fusion of all the features is carried out at the feature level. The multiclass SVM and KNN classifiers are used to recognize the facial expressions, effectively.&nbsp;<strong>Findings:</strong>&nbsp;In this work, an effort is made to develop a robust FER model using JAFFE database. It is recorded that, based on the experimental results, the proposed model suits better with a performance rate of 98.26% for SVM and 96.51% for KNN, when compared with the different state-of-the-art methods.&nbsp;<strong>Novelty:</strong>&nbsp;Many FER models have been developed and adopted for enhancing their quality and to extract the facial features using transform and frequency domains. It is observed that, maximum approaches are based on generating the texture features. The fusion at the feature level using modified HoG, LBP and FKBD is performed and the SVM model is more compatible when compared with other classifiers and it supports one-to-one and one-to-many comparisons&rsquo; technique.<strong>Keywords:</strong> Face Expression Recognition; Local Binary Pattern; Emotions; Nearest Neighbor; Histogram of Gradients
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Yang, Yu Han, and Yao Qin Xie. "Feature-Based GDLOH Deformable Registration for CT Lung Image." Applied Mechanics and Materials 333-335 (July 2013): 969–73. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.969.

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To improve the efficiency and accuracy of the conventional SIFT-TPS (Scale-invariant feature transform and Thin-Plate Spline) method in deformable registration for CT lung image, we develop a novel approach by using combining SURF(Speeded up Robust Features) and GDLOH(Gradient distance-location-orientation histogram) to detect matching feature points. First, we employ SURF as feature detection to find the stable feature points of the two CT images rapidly. Then GDLOH is taken as feature descriptor to describe each detected points characteristic, in order to supply measurement tool for matching process. In our experiment, five couples of clinical images are simulated using our algorithm above, result in an obvious improvement in run-time and registration quality, compared with the conventional methods. It is demonstrated that the proposed method may create a new window in performing a good robust and adaptively for deformable registration for CT lung tomography.
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Lu, Rui Tao, Xin Sheng Huang, Wan Ying Xu, and Lu Rong Shen. "Meanshift Tracking with Kalman Filter and Rotation-Invariant Features." Applied Mechanics and Materials 380-384 (August 2013): 1824–28. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1824.

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This paper presents an improve Meanshift tracking algorithm based on Kalman filter and Rotation-Invariant Features. Firstly, this paper forecasts the original alternate position using Kalman filter. Secondly, a kind of spatial histogram based on GRA (Gradient Radius Angle, GRA) is introduced, which is a rotation-invariant descriptor combined the information of gradient. At last, this paper searches the scale factor by the normal scale space decomposition technique. Experiments show that the method concerned in this paper has better performances than two improved algorithms.
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Chen, Xinqiang, Shengzheng Wang, Chaojian Shi, Huafeng Wu, Jiansen Zhao, and Junjie Fu. "Robust Ship Tracking via Multi-view Learning and Sparse Representation." Journal of Navigation 72, no. 1 (2018): 176–92. http://dx.doi.org/10.1017/s0373463318000504.

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Conventional visual ship tracking methods employ single and shallow features for the ship tracking task, which may fail when a ship presents a different appearance and shape in maritime surveillance videos. To overcome this difficulty, we propose to employ a multi-view learning algorithm to extract a highly coupled and robust ship descriptor from multiple distinct ship feature sets. First, we explore multiple distinct ship feature sets consisting of a Laplacian-of-Gaussian (LoG) descriptor, a Local Binary Patterns (LBP) descriptor, a Gabor filter, a Histogram of Oriented Gradients (HOG) descriptor and a Canny descriptor, which present geometry structure, texture and contour information, and more. Then, we propose a framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively. Finally, our framework is evaluated in four typical maritime surveillance scenarios. The experimental results show that the proposed framework outperforms the conventional and typical ship tracking methods.
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Dong, Jun, Xue Yuan, and Fanlun Xiong. "Global and Local Oriented Edge Magnitude Patterns for Texture Classification." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 03 (2017): 1750007. http://dx.doi.org/10.1142/s0218001417500070.

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In this paper, we propose a gray-scale texture descriptor, name the global and local oriented edge magnitude patterns (GLOEMP), for texture classification. GLOEMP is a framework, which is able to effectively combine local texture, global structure information and contrast of texture images. In GLOEMP, the principal orientation is determined by Histogram of Gradient (HOG) feature, then each direction is respectively shown in detail by a local binary patterns (LBP) occurrence histogram. Due to the fact that GLOEMP characterizes image information across different directions, it contains very abundant information. The global-level rotation compensation method is proposed, which shifts the principal orientation of the HOG to the first position, thus allowing GLOEMP to be robust to rotations. In addition, gradient magnitudes are used as weights to add to the histogram, making GLOEMP robust to lighting variances as well, and it also possesses a strong ability to express edge information. The experimental results obtained from the representative databases demonstrate that the proposed GLOEMP framework is capable of achieving significant improvement, in some cases reaching classification accuracy of 10% higher than over the traditional rotation invariant LBP method.
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Zhang, Li Hong, and Lin Li. "Improved Pedestrian Detection Based on Extended Histogram of Oriented Gradients." Applied Mechanics and Materials 347-350 (August 2013): 3815–20. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3815.

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In order to further improve pedestrian detection accuracy and avoid the disadvantage of original histogram of oriented gradients (HOG), differential template, overlap ratio and normalization method and so on are improved when HOG features are extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Multi-scale detection technique and non maxima suppression method are employed for precisely locating the pedestrians in the image. Experiments show that the human detection system improves detection accuracy and still maintains a relatively satisfactory speed.
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Sungin, Behram Khan, Ahmad Gulzar, Ali Faheem, Faisal Farooq, Ahmed Irfan, and Elah Salman. "Classification Performance of Linear Binary Pattern and Histogram Oriented Features for Arabic Characters Images: A Review." International Journal of Engineering Works 5, no. 4 (2018): 56–60. https://doi.org/10.5281/zenodo.1210109.

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There are millions of texts store in both off line and online forms. To utilize these documents properly, there is need of organizing these documents systematically and lots of applications are available for this purpose. Text classification is an important area of image processing deal with how the document belongs to its suitable class or category. Like other languages, Arabic language is also very rich and complex inflectional language which makes Arabic language very complex for ordinary analysis. In this review paper, we focus on the published research, especially in the field of Arabic text classification. Regard these all, three different types of feature extraction techniques are also implemented to extract features from different images of Arabic characters and presents a performance results of these techniques. From the result, it can be concluded that the combination of Linear binary pattern descriptor and Legendre moment, based moments features outperform and increase the accuracy of the LBP classifiers from 91.99 % to 93.12%
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CHEN, LIANG-HUA, LI-YUN WANG, and CHIH-WEN SU. "HUMAN DETECTION IN SURVEILLANCE VIDEO." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 02 (2014): 1455003. http://dx.doi.org/10.1142/s0218001414550039.

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In this paper, we propose an integrated approach for human detection in surveillance video. In our approach, the moving object is extracted by background subtraction; and the background model is updated by the first-order recurrence filter. Then, two complementary features are extracted for moving object classification. They are contour-based description: Fourier descriptor and region-based description: histogram of oriented gradient. As the binary classifier (support vector machine) is able to provide the posterior probability, we effectively integrate two types of features to achieve better performance. Experimental results show that the proposed approach is effective and outperforms some existing technique.
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Kittikhun, Meethongjan, Surinwarangkoon Thongchai, and Truong Hoang Vinh. "Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 6 (2020): 3019~3025. https://doi.org/10.12928/TELKOMNIKA.v18i6.16133.

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Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we require a quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by Histograms of Oriented Gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.
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Thimmegowda, Thirthe Gowda Mallinathapura, and Chandrika Jayaramaiah. "Cluster-based segmentation for tobacco plant detection and classification." Bulletin of Electrical Engineering and Informatics 12, no. 1 (2023): 75–85. http://dx.doi.org/10.11591/eei.v12i1.4388.

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Tobacco is one of the major economical crops in the agriculture sector. It is essential to detect tobacco plants using unmanned aerial vehicle (UAV) images for improved crop yield and plays an important role in the early treatment of tobacco plants. The proposed research work is carried out in three phases: In the first phase, we collect images from UAV’s and apply the French Commision Internationale de l'eclairage (CIE) L*a*b colour space model as pre-processing operations and segmentation. And then two prominent motion descriptors namely histogram of flow (HOF) and motion boundary histogram (MBH) are combined with the optimal histogram of oriented gradients (HOG) descriptor for exploring optimal motion trajectory and spatial measurements. And finally, the spatial variations with respect to the scale and illumination changes are incorporated using the optimal HOG descriptor. Here both dense motion patterns and HOG are refined using hierarchical feature selection using principal component analysis (PCA). The proposed model is trained and evaluated on different tobacco UAV image datasets and done a comparative analysis of different machine learning (ML) algorithms. The proposed model achieves good performance with 95% accuracy and 92% of sensitivity.
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Mahum, Rabbia, Saeed Ur Rehman, Talha Meraj, et al. "A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis." Sensors 21, no. 18 (2021): 6189. http://dx.doi.org/10.3390/s21186189.

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In the recent era, various diseases have severely affected the lifestyle of individuals, especially adults. Among these, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life. KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies. To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification. Firstly, the input X-ray images are preprocessed, and then the Region of Interest (ROI) is extracted through segmentation. Secondly, features are extracted from preprocessed X-ray images containing knee joint space width using hybrid feature descriptors such as Convolutional Neural Network (CNN) through Local Binary Patterns (LBP) and CNN using Histogram of oriented gradient (HOG). Low-level features are computed by HOG, while texture features are computed employing the LBP descriptor. Lastly, multi-class classifiers, that is, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), are used for the classification of KOA according to the Kellgren–Lawrence (KL) system. The Kellgren–Lawrence system consists of Grade I, Grade II, Grade III, and Grade IV. Experimental evaluation is performed on various combinations of the proposed framework. The experimental results show that the HOG features descriptor provides approximately 97% accuracy for the early detection and classification of KOA for all four grades of KL.
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49

Ma, Biao, and Minghui Ji. "Motion Feature Retrieval in Basketball Match Video Based on Multisource Motion Feature Fusion." Advances in Mathematical Physics 2022 (January 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/9965764.

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Both the human body and its motion are three-dimensional information, while the traditional feature description method of two-person interaction based on RGB video has a low degree of discrimination due to the lack of depth information. According to the respective advantages and complementary characteristics of RGB video and depth video, a retrieval algorithm based on multisource motion feature fusion is proposed. Firstly, the algorithm uses the combination of spatiotemporal interest points and word bag model to represent the features of RGB video. Then, the directional gradient histogram is used to represent the feature of the depth video frame. The statistical features of key frames are introduced to represent the histogram features of depth video. Finally, the multifeature image fusion algorithm is used to fuse the two video features. The experimental results show that multisource feature fusion can greatly improve the retrieval accuracy of motion features.
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50

S, Bhavana, H. K. Sandeep Kumar Reddy, Harshita Kulkarni, and Kusuma J. "AUTOMATIC FACE MASK DETECTION AND TEMPERATURE SCANNER." International Research Journal of Computer Science 9, no. 8 (2022): 173–78. http://dx.doi.org/10.26562/irjcs.2022.v0908.004.

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Face masks detection and Temperature scanner is an IoT device, that prevents the spread of Corona virus to people. This is done by ensuring that each and everyone are wearing mask and body temperature is less than the ambient temperature. Mask monitoring often requires additional staff resources. Some of the symptoms of covid-19 include fever, tiredness, sore throat, nasal congestion, loss of taste and smell. In most cases, it's transmitted directly (person to person) through respiratory droplets, but also indirectly via surfaces. Therefore, by using face mask and sanitizer, we can prevent the spread of corona virus. However, the crucial problem is that they are lack of approved vaccine and drugs to fight against corona virus. Histogram of oriented gradients (HOG) is a feature descriptor used to detect objects in computer vision and image processing. The HOG descriptor technique counts occurrences of gradient orientation in localized portions of an image - detection window, or region of interest (ROI). This helps in accurate detection of mask. MLX90614 is a temperature sensor, that efficiently detects the body temperature of a person.
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