Journal articles: 'Pyramid histogram of oriented gradients (PHOG)'

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Mutia, Cut, Fitri Arnia, and Rusdha Muharar. "Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG." Bulletin of Electrical Engineering and Informatics 6, no. 3 (September 1, 2017): 271–80. http://dx.doi.org/10.11591/eei.v6i3.657.

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The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories.

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Gour, Neha, and Pritee Khanna. "Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors." Pattern Recognition Letters 137 (September 2020): 3–11. http://dx.doi.org/10.1016/j.patrec.2019.04.004.

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Yan, Chao, Frans Coenen, and Bai Ling Zhang. "Driving Posture Recognition by Joint Application of Motion History Image and Pyramid Histogram of Oriented Gradients." Advanced Materials Research 846-847 (November 2013): 1102–5. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1102.

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This paper presents a novel approach to vision-based driving posture recognition. A driving action dataset was prepared by a side-mounted camera looking at a driver's left profile. The driving actions, including operating the shift lever, talking on a cell phone, eating and smoking, are decomposed into a number of predefined action primitives, which include operation of the shift lever, interaction with the drivers head and interaction with the dashboard. A global grid-based representation for the action primitives was emphasized, which first generate the silhouette shape from the motion history image, followed by application of the Pyramid Histogram of Oriented Gradients (PHOG) for more discriminating characterization. The random forest (RF) classifier was then exploited to classify the action primitives. Comparisons with some other commonly applied classifiers, such as kNN, multiple layer perceptron (MLP) and support vector machine (SVM), were provided. Classification accuracy is over 95% for the RF classifier in holdout experiment on the four manually decomposed driving actions.

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Yan, Chao, Frans Coenen, and Bailing Zhang. "Driving Posture Recognition by Joint Application of Motion History Image and Pyramid Histogram of Oriented Gradients." International Journal of Vehicular Technology 2014 (January 28, 2014): 1–11. http://dx.doi.org/10.1155/2014/719413.

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In the field of intelligent transportation system (ITS), automatic interpretation of a driver’s behavior is an urgent and challenging topic. This paper studies vision-based driving posture recognition in the human action recognition framework. A driving action dataset was prepared by a side-mounted camera looking at a driver’s left profile. The driving actions, including operating the shift lever, talking on a cell phone, eating, and smoking, are first decomposed into a number of predefined action primitives, that is, interaction with shift lever, operating the shift lever, interaction with head, and interaction with dashboard. A global grid-based representation for the action primitives was emphasized, which first generate the silhouette shape from motion history image, followed by application of the pyramid histogram of oriented gradients (PHOG) for more discriminating characterization. The random forest (RF) classifier was then exploited to classify the action primitives together with comparisons to some other commonly applied classifiers such as kNN, multiple layer perceptron, and support vector machine. Classification accuracy is over 94% for the RF classifier in holdout and cross-validation experiments on the four manually decomposed driving actions.

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Zhao, Zheng-Yang, Wen-Zhun Huang, Xin-Ke Zhan, Jie Pan, Yu-An Huang, Shan-Wen Zhang, and Chang-Qing Yu. "An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints." BioMed Research International 2021 (April 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/9933873.

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Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

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ZHANG, BAILING, and YIFAN ZHOU. "VEHICLE TYPE AND MAKE RECOGNITION BY COMBINED FEATURES AND ROTATION FOREST ENSEMBLE." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 03 (May 2012): 1250004. http://dx.doi.org/10.1142/s0218001412500048.

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Vehicle type/make recognition based on images captured by surveillance cameras is a challenging task in intelligent transportation system and automatic surveillance. In this paper, we comparatively studied two feature extraction methods for image description, i.e. a new multiresolution analysis tool called Fast Discrete Curvelet Transform and the pyramid histogram of oriented gradients (PHOG). Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform, which is particularly appropriate for the description of images rich with edges. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantized into a number of bins, thus has the ascendency in its description of more discriminating information. A composite feature description from PHOG and Curvelet can further increase the accuracy of classification by taking their complementary information. We also investigated the applicability of the Rotation Forest (RF) ensemble method for vehicle classification based on the combined features. The RF ensemble contains a set of base multilayer perceptrons which are trained using principal component analysis to rotate the original axes of combined features of vehicle images. The class label is assigned by the ensemble via majority voting. Experimental results using more than 600 images from 21 makes of cars/vans show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With a moderate ensemble size of 20, the Rotation Forest ensembles offers a classification rate close to 96.5%, exhibiting promising potentials for real-life applications.

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Acharya, U. Rajendra, Yuki Hagiwara, Joel E. W. Koh, Jen Hong Tan, Sulatha V. Bhandary, A. Krishna Rao, and U. Raghavendra. "Automated screening tool for dry and wet age-related macular degeneration (ARMD) using pyramid of histogram of oriented gradients (PHOG) and nonlinear features." Journal of Computational Science 20 (May 2017): 41–51. http://dx.doi.org/10.1016/j.jocs.2017.03.005.

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ZHANG, BAILING. "RELIABLE IMAGE CLASSIFICATION BY COMBINING FEATURES AND RANDOM SUBSPACE SUPPORT VECTOR MACHINE ENSEMBLE." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 03 (May 2014): 1450005. http://dx.doi.org/10.1142/s0218001414500050.

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We investigate the implementation of image categorization algorithms with a reject option, as a mean to enhance the system reliability and to attain a higher classification accuracy. A reject option is desired in many image-classification applications for which the system should abstain from making decisions on the most uncertain images. Based on the random subspace (RS) ensemble learning model, a highly reliable image classification scheme is proposed by applying RS support vector machine (SVM) ensemble. Being different to previous classifier ensembles which focus on increasing classification accuracy exclusively, the objective of the proposed SVM ensemble is to provide classification confidence and implement reject option to accommodate the situations where no decision should be made. The ensemble is created with four different feature descriptions, including local binary pattern (LBP), pyramid histogram of oriented gradient (PHOG), Gabor filtering and curvelet transform. The consensus degree from the ensemble's voting conforms to the confidence measure and the rejection option is accomplished accordingly when the confidence falls below a threshold. The reliable recognition scheme is empirically evaluated on three image categorization benchmark databases, including the face database created by Aleix Martinez and Robert Benavente (AR faces), a subset of Caltech-101 images for object classification, and 15 natural scene categories, all of which yielded consistently high reliable results, thus demonstrating the effectiveness of the proposed approach. For example, a 99.9% accuracy was obtained with a rejection rate of 2.5% for the AR faces, which exhibit promising potentials for real-world applications.

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Saha, Soumyajit, Manosij Ghosh, Soulib Ghosh, Shibaprasad Sen, Pawan Kumar Singh, Zong Woo Geem, and Ram Sarkar. "Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm." Applied Sciences 10, no. 8 (April 19, 2020): 2816. http://dx.doi.org/10.3390/app10082816.

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Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.

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Jia, Shi Jie, Yan Ping Yang, Jian Ying Zhao, and Nan Xiao. "Pyramid Histograms of Orientated Gradients for Product Image Retrieval." Advanced Materials Research 383-390 (November 2011): 5712–16. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5712.

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Traditional text-based image retrieval methods are hard to meet the requirements of on-line product search. This paper applied Content Based Image Retrieval (CBIR) technologies to e-commerce field and designed a product image retrieval algorithm based on Pyramid Histograms of Orientated Gradients (PHOG) descriptor and chi-square distance. By constructing the image retrieval system, we made retrieval tests on PI100 dataset from Microsoft Research Asia. The experimental results proved the efficiency of this algorithm.

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Jia, Shi Jie, Yu Ting Zhai, and Xiao Wei Jia. "Detection of Traffic and Road Condition Based on Adaboost." Applied Mechanics and Materials 433-435 (October 2013): 1357–60. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1357.

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In order to avoid the drawbacks of physical detection methods, such as spoiling the road, having complex algorithms and affected by weather factors, the detection methods of traffic and road condition are explored using the Adaboost algorithm and its three variants based on PHOG (pyramid histogram of edge orientation gradients) image feature. Experimental results show that this method can effectively classify 4 types of traffic and road condition images, and Gentle Adaboost algorithm has the best performance for the noisy samples.

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HuiMing Huang, HeSheng Liu, and GuoPing Liu. "Face Recognition Using Pyramid Histogram of Oriented Gradients and SVM." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 18 (October 31, 2012): 1–8. http://dx.doi.org/10.4156/aiss.vol4.issue18.1.

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Zhang, Bailing. "Off‐line signature verification and identification by pyramid histogram of oriented gradients." International Journal of Intelligent Computing and Cybernetics 3, no. 4 (November 23, 2010): 611–30. http://dx.doi.org/10.1108/17563781011094197.

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Guo, Shaojun, Feng Liu, Xiaohu Yuan, Chunrong Zou, Li Chen, and Tongsheng Shen. "HSPOG: An optimized target recognition method based on histogram of spatial pyramid oriented gradients." Tsinghua Science and Technology 26, no. 4 (August 2021): 475–83. http://dx.doi.org/10.26599/tst.2020.9010011.

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Zhang, Bailing, Yonghua Song, Sheng-uei Guan, and Yanchun Zhang. "Historic Chinese Architectures Image Retrieval by SVM and Pyramid Histogram of Oriented Gradients Features." International Journal of Soft Computing 5, no. 2 (February 1, 2010): 19–28. http://dx.doi.org/10.3923/ijscomp.2010.19.28.

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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 (July 4, 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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Win, Khin Yadanar, Noppadol Maneerat, Kazuhiko Hamamoto, and Syna Sreng. "Hybrid Learning of Hand-Crafted and Deep-Activated Features Using Particle Swarm Optimization and Optimized Support Vector Machine for Tuberculosis Screening." Applied Sciences 10, no. 17 (August 20, 2020): 5749. http://dx.doi.org/10.3390/app10175749.

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Tuberculosis (TB) is a leading infectious killer, especially for people with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Early diagnosis of TB is crucial for disease treatment and control. Radiology is a fundamental diagnostic tool used to screen or triage TB. Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled experts in remote areas. We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model. Then, six sets of hand-crafted features from statistical textures, local binary pattern, GIST, histogram of oriented gradients (HOG), pyramid histogram of oriented gradients and bags of visual words (BoVW), and nine sets of deep-activated features from AlexNet, GoogLeNet, InceptionV3, XceptionNet, ResNet-50, SqueezeNet, ShuffleNet, MobileNet, and DenseNet, were extracted. The dominant features of each feature set were selected using particle swarm optimization, and then separately input to an optimized support vector machine classifier to label ‘normal’ and ‘TB’ x-rays. GIST, HOG, BoVW from hand-crafted features, and MobileNet and DenseNet from deep-activated features performed better than the others. Finally, we combined these five best-performing feature sets to build a hybrid-learning algorithm. Using the Montgomery County (MC) and Shenzen datasets, we found that the hybrid features of GIST, HOG, BoVW, MobileNet and DenseNet, performed best, achieving an accuracy of 92.5% for the MC dataset and 95.5% for the Shenzen dataset.

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Li, Xin, Jie Chen, Ziguan Cui, Minghu Wu, and Xiuchang Zhu. "Single Image Super-Resolution Based on Sparse Representation with Adaptive Dictionary Selection." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 07 (May 25, 2016): 1654006. http://dx.doi.org/10.1142/s0218001416540069.

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Sparse representation theory has attracted much attention, and has been successfully used in image super-resolution (SR) reconstruction. However, it could only provide the local prior of image patches. Field of experts (FoE) is a way to develop the generic and expressive prior of the whole image. The algorithm proposed in this paper uses the FoE model as the global constraint of SR reconstruction problem to pre-process the low-resolution image. Since a single dictionary could not accurately represent different types of image patches, our algorithm classifies the sample patches composed of pre-processed image and high-resolution image, obtains the sub-dictionaries by training, and adaptively selects the most appropriate sub-dictionary for reconstruction according to the pyramid histogram of oriented gradients feature of image patches. Furthermore, in order to reduce the computational complexity, our algorithm makes use of edge detection, and only applies SR reconstruction based on sparse representation to the edge patches of the test image. Nonedge patches are directly replaced by the pre-processing results of FoE model. Experimental results show that our algorithm can effectively guarantee the quality of the reconstructed image, and reduce the computation time to a certain extent.

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Cui, Weihong, Guofeng Wang, Chenyi Feng, Yiwei Zheng, Jonathan Li, and Yi Zhang. "SPMK AND GRABCUT BASED TARGET EXTRACTION FROM HIGH RESOLUTION REMOTE SENSING IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 195–203. http://dx.doi.org/10.5194/isprs-archives-xli-b7-195-2016.

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Target detection and extraction from high resolution remote sensing images is a basic and wide needed application. In this paper, to improve the efficiency of image interpretation, we propose a detection and segmentation combined method to realize semi-automatic target extraction. We introduce the dense transform color scale invariant feature transform (TC-SIFT) descriptor and the histogram of oriented gradients (HOG) & HSV descriptor to characterize the spatial structure and color information of the targets. With the k-means cluster method, we get the bag of visual words, and then, we adopt three levels’ spatial pyramid (SP) to represent the target patch. After gathering lots of different kinds of target image patches from many high resolution UAV images, and using the TC-SIFT-SP and the multi-scale HOG & HSV feature, we constructed the SVM classifier to detect the target. In this paper, we take buildings as the targets. Experiment results show that the target detection accuracy of buildings can reach to above 90%. Based on the detection results which are a series of rectangle regions of the targets. We select the rectangle regions as candidates for foreground and adopt the GrabCut based and boundary regularized semi-auto interactive segmentation algorithm to get the accurate boundary of the target. Experiment results show its accuracy and efficiency. It can be an effective way for some special targets extraction.

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Dong, Chao, Jinghong Liu, Fang Xu, and Chenglong Liu. "Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor." Remote Sensing 11, no. 13 (June 28, 2019): 1529. http://dx.doi.org/10.3390/rs11131529.

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Automatic ship detection by Unmanned Airborne Vehicles (UAVs) and satellites is one of the fundamental challenges in maritime research due to the variable appearances of ships and complex sea backgrounds. To address this issue, in this paper, a novel multi-level ship detection algorithm is proposed to detect various types of offshore ships more precisely and quickly under all possible imaging variations. Our object detection system consists of two phases. First, in the category-independent region proposal phase, the steerable pyramid for multi-scale analysis is performed to generate a set of saliency maps in which the candidate region pixels are assigned to high salient values. Then, the set of saliency maps is used for constructing the graph-based segmentation, which can produce more accurate candidate regions compared with the threshold segmentation. More importantly, the proposed algorithm can produce a rather smaller set of candidates in comparison with the classical sliding window object detection paradigm or the other region proposal algorithms. Second, in the target identification phase, a rotation-invariant descriptor, which combines the histogram of oriented gradients (HOG) cells and the Fourier basis together, is investigated to distinguish between ships and non-ships. Meanwhile, the main direction of the ship can also be estimated in this phase. The overall algorithm can account for large variations in scale and rotation. Experiments on optical remote sensing (ORS) images demonstrate the effectiveness and robustness of our detection system.

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"Small Human Group Detection and Validation using Pyramidal Histogram of Oriented Gradients and Gray Level Run Length Method." International Journal of Engineering and Advanced Technology 9, no. 2 (December 30, 2019): 2387–94. http://dx.doi.org/10.35940/ijeat.a2252.129219.

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Over the decade’s human detection in security and surveillance system became dynamic research part in computer vision. This concern is focused by wide functions in several areas such as smart surveillance, multiple human interface, human pose characterization, person counting and person identification etc. Video surveillance organism mainly deals with recognition plus classification of moving objects with respect to several actions like walking, talking and hand shaking etc. The specific processing stages of small human group detection and validation includes frame generation, segmentation using hierarchical clustering, To achieve accurate classification feature descriptors namely Multi-Scale Completed Local Binary Pattern (MS-CLBP) and Pyramidal Histogram Of Oriented Gradients (PHOG) are employed to extract the features efficiently, Recurrent Neural Network (RNN) classifier helps to classify the features into human and group in a crowd, To extract statistical features Gray Level Run Length Method (GLRLM) is incorporated which helps in group validation.

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Sharifnejad, Maede, Asadollah Shahbahrami, Alireza Akoushideh, and Reza Zare Hassanpour. "Facial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction." IET Image Processing, December 14, 2020. http://dx.doi.org/10.1049/ipr2.12037.

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Hu, Xiuhua, Yuan Chen, Yan Hui, Yingyu Liang, Guiping Li, and Changyuan Wang. "Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features." International Journal of Pattern Recognition and Artificial Intelligence, September 13, 2020, 2155002. http://dx.doi.org/10.1142/s0218001421550028.

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Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

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Journal articles on the topic 'Pyramid histogram of oriented gradients (PHOG)'

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