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Xu, Xiaochun, Bin Li, and Q. M. Jonathan Wu. "A Completed Multi-Scale Local Statistics Pattern for Texture Classification." Image Analysis and Stereology 43, no. 3 (2024): 277–93. http://dx.doi.org/10.5566/ias.3037.
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Binary pattern methods play a vital role in extracting texture features. However, most of existing methods struggle to capture comprehensive and discriminative texture information. This paper aims to propose a novel multi-statistic binary pattern to extract rotation invariance statistic features for texture classification. First, this paper encodes the center pixel, mean, variance and range of local neighborhood by corresponding multi-scale threshold, and proposes the local center pattern, local mean pattern, local variance pattern and local range pattern. Then, based on the compact multi-pattern encoding strategy, the four sub-patterns are jointly encoded in a 4-bit binary pattern, named as multi-scale local statistics pattern. Finally, for comprehensive texture representation, the multi-scale local statistics pattern is jointly combined with local sign pattern and local magnitude pattern to generate a completed multi-scale local statistics pattern for texture classification. Extensive experiments conducted on three representative databases demonstrate that the proposed completed multi-scale local statistics pattern achieves competitive classification performance compared with other state-of-the-art approaches.
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Xing, Ning, and Lan Xian Gui. "Preprocessing for Local Bianry Pattern Based Face Recognition." Advanced Materials Research 760-762 (September 2013): 1434–37. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1434.
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This paper studies the pre-processing methods on Local Binary Pattern (LBP) for face recognition. Three methods are well investigated, such as Gaussian smoothing, histogram equalization and Sobel operator. The extensive experiments on FERET database show that the best recognition rate increase is achieved by combination of histogram equalization, Gaussian smoothing, and Sobel with LBP. The conclusion achieved in the paper is useful for real face recognition system.
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SHARIATMADAR, ZAHRA S., and KARIM FAEZ. "FINGER-KNUCKLE-PRINT RECOGNITION VIA ENCODING LOCAL-BINARY-PATTERN." Journal of Circuits, Systems and Computers 22, no. 06 (2013): 1350050. http://dx.doi.org/10.1142/s0218126613500503.
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Biometrics-based authentication is an effective approach which is used for automatically recognizing a person's identity. Recently, it has been found that the finger-knuckle-print (FKP), which refers to the texture pattern produced by the finger knuckle bending, is highly unique and can be used as a biometric identifier. In this paper, we present an effective FKP recognition scheme for personal identification and identity verification. This method is a new encoding scheme based on local binary pattern (LBP). Each image first is decomposed in several blocks, each block is convolved with a bank of Gabor filters and then, the LBPs histograms are extracted from the convolved images. Finally, a BioHashing approach is applied on the obtained fixed-length feature vectors. Extensive experiments conducted over the Poly-U FKP database demonstrated the efficiency and effectiveness of our proposed method.
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Hardeep, Singh, and Gagandeep. "Local Binary Patterns and Its Application to Facial Analysis." International Journal of Engineering Research and Reviews 10, no. 3 (2022): 11–20. https://doi.org/10.5281/zenodo.7014367.
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<strong>Abstract:</strong> This paper focuses on the Local Binary Patterns and its application to facial analysis. LBP is a non-parametric descriptor and used to extract, analyze, recognize and classify the different modality images. It summarizes the local patterns of image characteristics efficiently. In image processing, we have to extract features from a set of different texture or facial images, the Local Binary Pattern is a descriptor to extract, analyze, recognize and classify that data. There are detection, face representation, face detection and face recognition processes needed to analyze a face, from which the data obtained of different faces are comparatively checked according a specific exact facial image of person. This technique is applied at biometric machine and at other purposes to recognize an authentic face image. This paper facial analysis process and different local binary pattern techniques applied for facial detection and recognition are extensively reviewed. <strong>Keywords:</strong> Local Binary Pattern, LBP, Face Detection, Recognition. <strong>Title:</strong> Local Binary Patterns and Its Application to Facial Analysis <strong>Author:</strong> Hardeep Singh, Gagandeep <strong>International Journal of Engineering Research and Reviews</strong> <strong>ISSN 2348-697X (Online)</strong> <strong>Vol. 10, Issue 3, July 2022 - September 2022</strong> <strong>Page No: 11-20</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 22-August-2022</strong> <strong>DOI: </strong><strong>https://doi.org/10.5281/zenodo.7014367</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/local-binary-patterns-and-its-application-to-facial-analysis</strong>
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Zeng, Hui, Xiuqing Wang, and Yu Gu. "Center Symmetric Local Multilevel Pattern Based Descriptor and Its Application in Image Matching." International Journal of Optics 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/1584514.
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This paper presents an effective local image region description method, called CS-LMP (Center Symmetric Local Multilevel Pattern) descriptor, and its application in image matching. The CS-LMP operator has no exponential computations, so the CS-LMP descriptor can encode the differences of the local intensity values using multiply quantization levels without increasing the dimension of the descriptor. Compared with the binary/ternary pattern based descriptors, the CS-LMP descriptor has better descriptive ability and computational efficiency. Extensive image matching experimental results testified the effectiveness of the proposed CS-LMP descriptor compared with other existing state-of-the-art descriptors.
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Mounika, Kavadapu, and M. Devendra. "Segementation of Blur Images Using Local Binary Pattern Technique." International Journal of Engineering & Technology 7, no. 4.7 (2018): 315. http://dx.doi.org/10.14419/ijet.v7i4.7.20569.
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Defocus blur is to a great degree regular in images caught utilizing optical imaging frameworks. It might be bothersome, however may likewise be a deliberate imaginative impact, in this manner it can either upgrade or hinder our visual view of the image scene. For assignments, for example, image restoration and object recognition, one should need to portion an in part blurred image into blurred and non-blurred areas. In this paper, we propose sharpness metric in light of local binary patterns and a hearty segmentation calculation to isolate all through focus image districts. The proposed sharpness metric adventures the perception that most local image fixes in blurry areas have altogether less of certain local binary patterns contrasted and those in sharp districts. Utilizing this metric together with image tangling and multiscale surmising, we got excellent sharpness maps. Tests on several halfway blurred images were utilized to assess our blur segmentation calculation and six comparator techniques. The outcomes demonstrate that our calculation accomplishes similar segmentation comes about with the best in class and have enormous speed advantage over the others. in Extension we are using LLBP (Line Local Binary Pattern ) for getting better output in blur images.
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Lian, Yaxin, and Yongsheng Dong. "Texture Classification Method Based on Local Enhancement and Non-local Median Patterns." Academic Journal of Science and Technology 11, no. 1 (2024): 88–96. http://dx.doi.org/10.54097/6xvc9784.
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Local binary pattern (LBP) serves as a highly powerful method for texture classification. LBP and its variant methods are extensively applied across various domains of image processing.In increasingly complex imaging environments, LBP faces two issues: (1) Loss of detail information during feature extraction. (2) Sensitivity to noise. To mitigate these issues, this paper proposes a method called Local Enhancement and Non-local Median Pattern (LENMP). It consists of two operators: Local Adaptive Contrast Enhancement Pattern (LEP) and Non-local Median Binary Pattern (NMBP). The LEP operator captures the sign and magnitude details of local image characteristics, while the NMBP operator captures the global information of image features. First, the image is processed using the threshold ACE (thACE) algorithm to enhance the contrast of high-frequency information in the image, extracting image contour edge information. Then, median extraction is performed separately on the two operators to capture larger spatial texture detail information. Finally, comparative experiments are conducted on multiple datasets (Outex, CUReT, Brodatz, KTH-TIPS, and Kylberg) with representative texture classification methods. The results indicate that our proposed LENMP texture classification method exhibits good classification performance and remains competitive compared to the latest descriptors.
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Rabidas, Rinku, Abhishek Midya, Jayasree Chakraborty, and Wasim Arif. "Multi-Resolution Analysis of Edge-Texture Features for Mammographic Mass Classification." Journal of Circuits, Systems and Computers 29, no. 10 (2019): 2050156. http://dx.doi.org/10.1142/s021812662050156x.
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In this paper, multi-resolution analysis of two edge-texture based descriptors, Discriminative Robust Local Binary Pattern (DRlbp) and Discriminative Robust Local Ternary Pattern (DRltp), are proposed for the determination of mammographic masses as benign or malignant. As an extension of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP), DRlbp and LTP-based features overcome the drawbacks of these features preserving the edge information along with texture. With the hypothesis that multi-resolution analysis of these features for different regions related to mammaographic masses with wavelet transform will capture more discriminating patterns and thus can help in characterizing masses. In order to evaluate the efficiency of the proposed approach, several experiments are carried out using the mini-MIAS database where a 5-fold cross validation technique is incorporated with Support Vector Machine (SVM) on the optimal set of features obtained via stepwise logistic regression method. An area under the receiver operating characteristic (ROC) curve ([Formula: see text] value) of 0.96 is achieved with DRlbp attributes as the best performance. The superiority of the proposed scheme is established by comparing the obtained results with recently developed other competing schemes.
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Chen, Shen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, and Rongrong Ji. "Local Relation Learning for Face Forgery Detection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1081–88. http://dx.doi.org/10.1609/aaai.v35i2.16193.
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With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision. However, without considering the correlation between local regions, these global supervisions are insufficient to learn a generalized feature and prone to overfitting. To address this issue, we propose a novel perspective of face forgery detection via local relation learning. Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern. Moreover, we propose an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation, which further improves the reliability of the similarity pattern. Extensive experiments show that the proposed method consistently outperforms the state-of-the-arts on widely-used benchmarks. Furthermore, detailed visualization shows the robustness and interpretability of our method.
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Khaleefah, Shihab Hamad, Salama A. Mostafa, Aida Mustapha, Noor Azah Samsudin, Mohammad Faidzul Nasrudin, and Abdullah Baz. "A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1379–87. https://doi.org/10.11591/ijeecs.v20.i3.pp1379-1387.
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With the dramatic expansion of image information nowadays, image processing and computer visions are playing a significant role in terms of several applications such as image classification, image segmentation, pattern recognition, and image retrieval. One of the important features that have been used in many image applications is texture. The texture is the characteristic of a set of pixels that formed the image. Therefore, analyzing such texture would have a significant impact on segmenting the image or detecting important portions of such image. This paper aims to overview the feature extraction and description techniques depicted in the literature to characterize regions for images. In particular, two of popular descriptors will be examined including Local Binary Pattern (LBP) and Gabor Filter. The key characteristic behind such investigation lies in how the features of an image would contribute toward the process of recognition and image classification. In this regard, an extensive discussion is provided on both LBP and Gabor descriptors along with the efforts that have been intended to combine them. The reason behind investigating these descriptors is that they are considered the most common local and global descriptors used in the literature. The overall aim of this survey is to show current trends on using, modifying and adapting these descriptors in the domain of image processing.
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Liu, Jing. "Does the Division of Domestic Value Chains Affect the Export Binary Margin." Frontiers in Business, Economics and Management 9, no. 3 (2023): 32–36. http://dx.doi.org/10.54097/fbem.v9i3.9484.
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This study examines the potential heterogeneous effects of domestic value chains (DVCs) on two different paths of export expansion, namely the intensive margin and the extensive margin. Using panel data of 195 prefecture-level cities in China spanning from 2000 to 2013, this paper explores the association between DVCs and export binary margin. The results indicate that DVCs exhibit a significant positive U-shaped relationship with the export intensive margin, while it has a linear positive effect on the export extensive margin. Additionally, market integration and market competition emerge as important mechanisms through which DVCs influence the city’s export binary margin. In addition, the study suggests that central and western regions' integration into DVCs more strongly promotes local export expansion compared to eastern regions. Overall, the empirical evidence presented in this paper contributes to enhancing the understanding of the development pattern of DVCs in China from the perspective of trade competitiveness.
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Feng, Qinghe, Qiaohong Hao, Mateu Sbert, Yugen Yi, Ying Wei, and Jiangyan Dai. "Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval." Sensors 19, no. 2 (2019): 315. http://dx.doi.org/10.3390/s19020315.
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Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in “parallel” and “cross” manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.
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ZHANG, WENCHAO, SHIGUANG SHAN, XILIN CHEN, and WEN GAO. "LOCAL GABOR BINARY PATTERNS BASED ON MUTUAL INFORMATION FOR FACE RECOGNITION." International Journal of Image and Graphics 07, no. 04 (2007): 777–93. http://dx.doi.org/10.1142/s021946780700291x.
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Appropriate representation is one of the keys to the success of face recognition technologies. In this paper, we present a novel face representation approach using a reduced set of local histograms based on Local Gabor Binary Patterns (LGBP). In the proposed method, a face image is first represented by the LGBP histograms which are extracted from the LGBP images. Then, the local LGBP histograms with high separability and low relevance are selected to obtain a dimension-reduced face descriptor. Extensive experimental results demonstrate that the proposed method not only greatly reduces the dimensionality of face representation, but also outperforms the state-of-the-art approaches for face recognition, such as Fisherfaces, and Gabor Fisher Classification (GFC).
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Hamad Khaleefah, Shihab, Salama A. Mostafa, Aida Mustapha, Noor Azah Samsudin, Mohammad Faidzul Nasrudin, and Abdullah Baz. "A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1379. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1379-1387.
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With the dramatic expansion of image information nowadays, image processing and computer visions are playing a significant role in terms of several applications such as image classification, image segmentation, pattern recognition, and image retrieval. One of the important features that have been used in many image applications is texture. The texture is the characteristic of a set of pixels that formed the image. Therefore, analyzing such texture would have a significant impact on segmenting the image or detecting important portions of such image. This paper aims to overview the feature extraction and description techniques depicted in the literature to characterize regions for images. In particular, two of popular descriptors will be examined including Local Binary Pattern (LBP) and Gabor Filter. The key characteristic behind such investigation lies in how the features of an image would contribute toward the process of recognition and image classification. In this regard, an extensive discussion is provided on both LBP and Gabor descriptors along with the efforts that have been intended to combine them. The reason behind investigating these descriptors is that they are considered the most common local and global descriptors used in the literature. The overall aim of this survey is to show current trends on using, modifying and adapting these descriptors in the domain of image processing.
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Ramakrishnan, Srinivasan, and Sivasamy Nithya. "Two improved extension of local binary pattern descriptors using wavelet transform for texture classification." IET Image Processing 12, no. 11 (2018): 2002–10. http://dx.doi.org/10.1049/iet-ipr.2018.5410.
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Al Saidi, Ibtissam, Mohammed Rziza, and Johan Debayle. "A New LBP Variant: Corner Rhombus Shape LBP (CRSLBP)." Journal of Imaging 8, no. 7 (2022): 200. http://dx.doi.org/10.3390/jimaging8070200.
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The local binary model is a straightforward, dependable, and effective method for extracting relevant local information from images. However, because it only uses sign information in the local region, the local binary pattern (LBP) is ineffective at capturing discriminating characteristics. Furthermore, most LBP variants select a region with one specific center pixel to fill all neighborhoods. In this paper, a new variant of a LBP is proposed for texture classification, known as corner rhombus-shape LBP (CRSLBP). In the CRSLBP approach, we first use three methods to threshold the pixel’s neighbors and center to obtain four center pixels by using sign and magnitude information with respect to a chosen region of an even block. This helps determine not just the relationship between neighbors and the pixel center but also between the center and the neighbor pixels of neighborhood center pixels. We evaluated the performance of our descriptors using four challenging texture databases: Outex (TC10,TC12), Brodatz, KTH-TIPSb2, and UMD. Various extensive experiments were performed that demonstrated the effectiveness and robustness of our descriptor in comparison with the available state of the art (SOTA).
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Slimani, Khadija, Mohamed Kas, Youssef El Merabet, Yassine Ruichek, and Rochdi Messoussi. "Local feature extraction based facial emotion recognition: a survey." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4080. http://dx.doi.org/10.11591/ijece.v10i4.pp4080-4092.
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Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.
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Khadija, Slimani, Kas Mohamed, El merabet Youssef, Ruichek Yassine, and Messoussi Rochdi. "Local feature extraction based facial emotion recognition: A survey." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4080–92. https://doi.org/10.11591/ijece.v10i4.pp4080-4092.
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Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of Lo- cal Binary Pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essen- tial attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several re- cent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively
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Liu, Peng-Yi, and Zhi-Ming Li. "A Feature Extraction Method based on Local Binary Pattern Preprocessing and Wavelet Transform." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 13 (2020): 2050030. http://dx.doi.org/10.1142/s0218001420500305.
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Face recognition has been extensively studied by many scholars in the recent decades. Local binary pattern (LBP) is one of the most popular local descriptors and has been widely applied to face recognition. Wavelet transform is also more and more active in the field of pattern recognition. In this paper, a novel feature extraction method is proposed to overcome illumination influence. First, a given face image is processed by the LBP operator, and an LBP image is obtained. Second, wavelet transform is used to extract discriminant feature from the LBP image. The experiment results on LFW, Extended YaleB and CMU-PIE face databases show that the proposed method outperforms several popular face recognition methods, and the preprocessing step plays an important role to extract effective features for classification.
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Norouzi Sefidmazgi, Akram, and Manoochehr Nahvi. "Improved background modeling of video sequences using spatio-temporal extension of fuzzy local binary pattern." Multimedia Tools and Applications 78, no. 12 (2019): 17287–316. http://dx.doi.org/10.1007/s11042-018-6972-7.
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Hardika, Khusnuliawati, Fatichah Chastine, and Soelaiman Rully. "Multi-feature Fusion Using SIFT and LEBP for Finger Vein Recognition." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 478–85. https://doi.org/10.12928/TELKOMNIKA.v15i1.4443.
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In this paper, multi-feature fusion using Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP) was proposed to obtain a feature that could resist degradation problems such as scaling, rotation, translation and varying illumination conditions. SIFT feature had a capability to withstand degradation due to changes in the condition of the image scale, rotation and translation. Meanwhile, LEBP feature had resistance to gray level variations with richer and discriminatory local characteristics information. Therefore the fusion technique is used to collect important information from SIFT and LEBP feature.The resulting feature of multi-feature fusion using SIFT and LEBP feature would be processed by Learning Vector Quantization (LVQ) method to determine whether the testing image could be recognized or not. The accuracy value could achieve 97.50%, TPR at 0.9400 and FPR at 0.0128 in optimum condition. That was a better result than only use SIFT or LEBP feature.
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XU, GANG, HUCHUAN LU, and ZUNYI WANG. "FACE RECOGNITION BASED ON GPPBTF AND LBP WITH CLASSIFIER FUSION." International Journal of Image and Graphics 12, no. 02 (2012): 1250011. http://dx.doi.org/10.1142/s0219467812500118.
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Robust face recognition is a challenging problem, due to facial appearance variations in illumination, pose, expression, aging, partial occlusions and other changes. This paper proposes a novel face recognition approach, where face images are represented by Gabor pixel-pattern-based texture feature (GPPBTF) and local binary pattern (LBP), and null pace-based kernel Fisher discriminant analysis (NKFDA) is applied to the two features independently to obtain two recognition results which are eventually combined together for a final identification. To get GPPBTF, we first transform an image into Gabor magnitude maps of different orientations and scales, and then use pixel-pattern-based texture feature to extract texture features from Gabor maps. In order to improve the final performance of the classification, this paper proposes a multiple NKFDA classifiers combination approach. Extensive experiments on FERET face database demonstrate that the proposed method not only greatly reduces the dimensionality of face representation, but also achieves more robust result and higher recognition accuracy.
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Romero-González, Cristina, Ismael García-Varea, and Jesus Martínez-Gómez. "Shape binary patterns: an efficient local descriptor and keypoint detector for point clouds." Multimedia Tools and Applications 81, no. 3 (2022): 3577–601. http://dx.doi.org/10.1007/s11042-021-11586-5.
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AbstractMany of the research problems in robot vision involve the detection of keypoints, areas with salient information in the input images and the generation of local descriptors, that encode relevant information for such keypoints. Computer vision solutions have recently relied on Deep Learning techniques, which make extensive use of the computational capabilities available. In autonomous robots, these capabilities are usually limited and, consequently, images cannot be processed adequately. For this reason, some robot vision tasks still benefit from a more classic approach based on keypoint detectors and local descriptors. In 2D images, the use of binary representations for visual tasks has shown that, with lower computational requirements, they can obtain a performance comparable to classic real-value techniques. However, these achievements have not been fully translated to 3D images, where research is mainly focused on real-value approaches. Thus, in this paper, we propose a keypoint detector and local descriptor based on 3D binary patterns. The experimentation demonstrates that our proposal is competitive against state-of-the-art techniques, while its processing can be performed more efficiently.
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Oktavia, Vivin, and Novan Wijaya. "Pengenalan Tulisan Tangan Huruf Latin Bersambung Menggunakan Local Binary Pattern dan K-Nearest Neighbor." JISKA (Jurnal Informatika Sunan Kalijaga) 7, no. 3 (2022): 211–25. http://dx.doi.org/10.14421/jiska.2022.7.3.211-225.
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There are 26 Latin letters in Indonesia, 5 of which are vowels and 21 consonants. This study will translate handwriting with a Latin object using the K-Nearest Neighbor method with the Local Binary Pattern extension. The research is being done with a focus on experimentation using a few methods that have already been discussed. Concatenated Latin letters have a few variations that depend on the work's author, so research will be conducted to identify cursive Latin letters based on these variations. Each of the 30 respondents wrote 26 capital letters and 26 lowercase letters on paper, which was then scanned to provide the image data. Black, blue, and red pens were used to write by every ten responders. The recognition procedure is broken into two halves, capital and non-capital letter recognition using 780 picture datasets each. In the study, k-fold cross-validation is used, with k = 6. The best value was reached at k = 7 with 29.49 percent accuracy, 33.88 percent precision, recall 33.46 percent, and F1-score 27.65 percent according to the research utilizing KNN with values k = 3, 5, and 7. and for recognizing non-capital characters, the best result was found at k=3 with accuracy, precision, recall, and F1-score of 26.28, 27.27, and 22.7%, respectively.
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Utomo, Budi Tri, Iskandar Fitri, and Eri Mardiani. "Penerapan Face Recognition pada Aplikasi Akademik Online." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 5, no. 4 (2021): 420. http://dx.doi.org/10.35870/jtik.v5i4.244.
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In the era of big data, the biometric identification process is growing very fast and is increasingly being implemented in many applications. Face recognition technology utilizes artificial intelligence (AI) to recognize faces that are already stored in the database. In this research, it is proposed to design an online academic login system at the National University using real time face recognition used OpenCV with the Local Binary Pattern Histogram algorithm and the Haar Cassade Classification method. The system will detect, recognize and compare faces with the stored face database. The image used is 480 x 680 pixels with a .jpg extension in the form of an RGB image which will be converted into a Grayscale image., to make it easier to calculate the histogram value of each face that will be recognized. With a modeling system like this it is hope to make it easy for user to log into online academics.Keywords:Face Recognition, Haar Cascade Clasifier, Local Binary Pattern Histogram, Online Akademic, OpenCV.
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Lemaître, Guillaume, Mojdeh Rastgoo, Joan Massich, et al. "Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection." Journal of Ophthalmology 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/3298606.
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This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.
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Deep, G., J. Kaur, Simar Preet Singh, Soumya Ranjan Nayak, Manoj Kumar, and Sandeep Kautish. "MeQryEP: A Texture Based Descriptor for Biomedical Image Retrieval." Journal of Healthcare Engineering 2022 (April 11, 2022): 1–20. http://dx.doi.org/10.1155/2022/9505229.
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Image texture analysis is a dynamic area of research in computer vision and image processing, with applications ranging from medical image analysis to image segmentation to content-based image retrieval and beyond. “Quinary encoding on mesh patterns (MeQryEP)” is a new approach to extracting texture features for indexing and retrieval of biomedical images, which is implemented in this work. An extension of the previous study, this research investigates the use of local quinary patterns (LQP) on mesh patterns in three different orientations. To encode the gray scale relationship between the central pixel and its surrounding neighbors in a two-dimensional (2D) local region of an image, binary and nonbinary coding, such as local binary patterns (LBP), local ternary patterns (LTP), and LQP, are used, while the proposed strategy uses three selected directions of mesh patterns to encode the gray scale relationship between the surrounding neighbors for a given center pixel in a 2D image. An innovative aspect of the proposed method is that it makes use of mesh image structure quinary pattern features to encode additional spatial structure information, resulting in better retrieval. On three different kinds of benchmark biomedical data sets, analyses have been completed to assess the viability of MeQryEP. LIDC-IDRI-CT and VIA/I–ELCAP-CT are the lung image databases based on computed tomography (CT), while OASIS-MRI is a brain database based on magnetic resonance imaging (MRI). This method outperforms state-of-the-art texture extraction methods, such as LBP, LQEP, LTP, LMeP, LMeTerP, DLTerQEP, LQEQryP, and so on in terms of average retrieval precision (ARP) and average retrieval rate (ARR).
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BASHAR, M. K., and N. OHNISHI. "WAVELET-BASED SALIENT ENERGY POINTS FOR UNSUPERVISED TEXTURE SEGMENTATION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 03 (2005): 429–58. http://dx.doi.org/10.1142/s0218001405004113.
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Despite extensive research on image texture analysis, it is still problematic to characterize and segment texture images especially in the presence of complex patterns. Upon tremendous advancement of the internet and the digital technology, there is also a need for the development of simple but efficient algorithms, which can be adaptable to real-time systems. In this study, we propose such an approach based on multiresolution discrete wavelet transform (DWT). After the transform, we compute salient energy points from each directional sub-band (LH, HL, and HH) in the form of binary image by thresholding intermittency indices of wavelet coefficients. We then propose and extract two new texture features namely Salient Point Density (SPD) and Salient Point Distribution Nonuniformity (SPDN) based on the number and the distribution of salient pixels in the local neighborhood of every pixel of the multiscale binary images. We thus obtain a set of feature images, which are subsequently applied to the popular K-means algorithm for the unsupervised segmentation of texture images. Though the above representation appear simple and infrequent in the literature, it proves useful in the context of texture segmentation. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potentiality of the proposed approach.
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Telli, Hichem, Salim Sbaa, Salah Eddine Bekhouche, Fadi Dornaika, Abdelmalik Taleb-Ahmed, and Miguel Bordallo López. "A Novel Multi-Level Pyramid Co-Variance Operators for Estimation of Personality Traits and Job Screening Scores." Traitement du Signal 38, no. 3 (2021): 539–46. http://dx.doi.org/10.18280/ts.380301.
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Recently, automatic personality analysis is becoming an interesting topic for computer vision. Many attempts have been proposed to solve this problem using time-based sequence information. In this paper, we present a new framework for estimating the Big-Five personality traits and job candidate screening variable from video sequences. The framework consists of two parts: (1) the use of Pyramid Multi-level (PML) to extract raw facial textures at different scales and levels; (2) the extension of the Covariance Descriptor (COV) to fuse different local texture features of the face image such as Local Binary Patterns (LBP), Local Directional Pattern (LDP), Binarized Statistical Image Features (BSIF), and Local Phase Quantization (LPQ). Therefore, the COV descriptor uses the textures of PML face parts to generate rich low-level face features that are encoded using concatenation of all PML blocks in a feature vector. Finally, the entire video sequence is represented by aggregating these frame vectors and extracting the most relevant features. The exploratory results on the ChaLearn LAP APA2016 dataset compare well with state-of-the-art methods including deep learning-based methods.
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Tian, Yuan, Zhao Wang, Di Chen, and Huang Yao. "TriCAFFNet: A Tri-Cross-Attention Transformer with a Multi-Feature Fusion Network for Facial Expression Recognition." Sensors 24, no. 16 (2024): 5391. http://dx.doi.org/10.3390/s24165391.
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In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention transformer with a multi-feature fusion network (TriCAFFNet) to improve facial expression recognition performance under challenging conditions. By combining LBP (Local Binary Pattern) features, HOG (Histogram of Oriented Gradients) features, landmark features, and CNN (convolutional neural network) features from facial images, the model is provided with a rich input to improve its ability to discern subtle differences between images. Additionally, tri-cross-attention blocks are designed to facilitate information exchange between different features, enabling mutual guidance among different features to capture salient attention. Extensive experiments on several widely used datasets show that our TriCAFFNet achieves the SOTA performance on RAF-DB with 92.17%, AffectNet (7 cls) with 67.40%, and AffectNet (8 cls) with 63.49%, respectively.
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Siddiqi, Muhammad Hameed, Khurshed Asghar, Umar Draz, et al. "Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns." Security and Communication Networks 2021 (September 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/4270776.
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With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.
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Li, Wenwen. "Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features." Journal of Healthcare Engineering 2022 (January 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/6041828.
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Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users’ satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.
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MAKRIDIS, MICHAEL, and N. PAPAMARKOS. "AN ADAPTIVE LAYER-BASED LOCAL BINARIZATION TECHNIQUE FOR DEGRADED DOCUMENTS." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 02 (2010): 245–79. http://dx.doi.org/10.1142/s0218001410007889.
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This paper presents a new technique for adaptive binarization of degraded document images. The proposed technique focuses on degraded documents with various background patterns and noise. It involves a preprocessing local background estimation stage, which detects for each pixel that is considered as background one, a proper grayscale value. Then, the estimated background is used to produce a new enhanced image having uniform background layers and increased local contrast. That is, the new image is a combination of background and foreground layers. Foreground and background layers are then separated by using a new transformation which exploits efficiently, both grayscale and spatial information. The final binary document is obtained by combining all foreground layers. The proposed binarization technique has been extensively tested on numerous documents and successfully compared with other well-known binarization techniques. Experimental results, which are based on statistical, visual and OCR criteria, verify the effectiveness of the technique.
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Gao, Ning, Xingyuan Wang, and Xiukun Wang. "Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement." Applied Sciences 9, no. 5 (2019): 977. http://dx.doi.org/10.3390/app9050977.
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Robust and accurate face alignment remains a challenging task, especially when local noises, illumination variations and partial occlusions exist in images. The existing local search and global match methods often misalign due to local optima without global constraints or limited local representation of global appearance. To solve these problems, we propose a new multi-layer progressive face alignment method that combines global matches for a whole face with local refinement for a given region, where the errors caused by local optima are restricted by globally-matched appearance, and the local misalignments in the global method are avoided by supplementing the representation of local details. Our method consists of the following processes: Firstly, an input image is encoded as a multi-mode Local Binary Pattern (LBP) image to regress the face shape parameters. Secondly, the local multi-mode histogram of oriented gradient (HOG) features is applied to update each landmark position. Thirdly, the above two alignment shapes are weighted as the final result. The contributions of this paper are as follows: (1) Shape initialization by applying an affine transformation to the mean shape. (2) Face representation by integrating multi-mode information in a whole face or a face region. (3) Face alignment by combining handcrafted features with convolutional neural networks (CNN). Extensive experiments on public datasets show that our method demonstrates improved performance in real environments in comparison to some state-of-the-art methods which apply single scale features or single CNN networks. Applying our method to the challenging HELEN dataset, the samples with fewer than 8 mean errors reach 81.1%.
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Mensah, Patrick Kwabena, Benjamin Asubam Weyori, and Mighty Abra Ayidzoe. "Capsule network with K-Means routingfor plant disease recognition." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 1025–36. http://dx.doi.org/10.3233/jifs-201226.
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Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a ‘simple-squash’ in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algorithm. Extensive experiments conducted on the three datasets showed that the proposed model achieves consistent improvement in test accuracy across the three datasets as well as allowing an increase in the number of routing iterations with no performance degradation. The proposed model outperformed a baseline CapsNet by 8.37% on the tomato dataset with an overall test accuracy of 98.80%, comparable to state-of-the-art models on the same datasets.
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Liu, Yanting, Zhe Xu, Yongjia Yu, and Xingzhi Chang. "A novel binary genetic differential evolution optimization algorithm for wind layout problems." AIMS Energy 12, no. 1 (2024): 321–49. http://dx.doi.org/10.3934/energy.2024016.
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<abstract><p>This paper addresses the increasingly critical issue of environmental optimization in the context of rapid economic development, with a focus on wind farm layout optimization. As the demand for sustainable resource management, climate change mitigation, and biodiversity conservation rises, so does the complexity of managing environmental impacts and promoting sustainable practices. Wind farm layout optimization, a vital subset of environmental optimization, involves the strategic placement of wind turbines to maximize energy production and minimize environmental impacts. Traditional methods, such as heuristic approaches, gradient-based optimization, and rule-based strategies, have been employed to tackle these challenges. However, they often face limitations in exploring the solution space efficiently and avoiding local optima. To advance the field, this study introduces LSHADE-SPAGA, a novel algorithm that combines a binary genetic operator with the LSHADE differential evolution algorithm, effectively balancing global exploration and local exploitation capabilities. This hybrid approach is designed to navigate the complexities of wind farm layout optimization, considering factors like wind patterns, terrain, and land use constraints. Extensive testing, including 156 instances across different wind scenarios and layout constraints, demonstrates LSHADE-SPAGA's superiority over seven state-of-the-art algorithms in both the ability of jumping out of the local optima and solution quality.</p></abstract>
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Bai, Shung, Jianjun Hou, and Noboru Ohnishi. "Scene Categorization Through Combining LBP and SIFT Features Effectively." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 01 (2015): 1655001. http://dx.doi.org/10.1142/s0218001416550016.
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In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.
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MUHAMMAD, GHULAM, MUHAMMAD HUSSAIN, FATMAH ALENEZY, GEORGE BEBIS, ANWAR M. MIRZA, and HATIM ABOALSAMH. "RACE CLASSIFICATION FROM FACE IMAGES USING LOCAL DESCRIPTORS." International Journal on Artificial Intelligence Tools 21, no. 05 (2012): 1250019. http://dx.doi.org/10.1142/s0218213012500194.
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This paper investigates and compares the performance of local descriptors for race classification from face images. Two powerful types of local descriptors have been considered in this study: Local Binary Patterns (LBP) and Weber Local Descriptors (WLD). First, we investigate the performance of LBP and WLD separately and experiment with different parameter values to optimize race classification. Second, we apply the Kruskal-Wallis feature selection algorithm to select a subset of more "discriminative" bins from the LBP and WLD histograms. Finally, we fuse LBP and WLD, both at the feature and score levels, to further improve race classification accuracy. For classification, we have considered the minimum distance classifier and experimented with three distance measures: City-block, Euclidean, and Chi-square. We have performed extensive experiments and comparisons using five race groups from the FERET database. Our experimental results indicate that (i) using the Kruskal-Wallis feature selection, (ii) fusing LBP with WLD at the feature level, and (iii) using the City-block distance for classification, outperforms LBP and WLD alone as well as methods based on holistic features such as Principal Component Analysis (PCA) and LBP or WLD (i.e., applied globally).
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admin, admin, and Subbiah Bharathi Venkatachalam. "A Machine Learning Approach for Automated Detection and Classification of Cracks in Ancient Monuments using Image Processing Techniques." Journal of Cybersecurity and Information Management 14, no. 2 (2024): 214–38. http://dx.doi.org/10.54216/jcim.140215.
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Stone monuments stand as enduring testaments to human history and cultural heritage, yet they are susceptible to deterioration over time. In this paper, we propose a comprehensive approach for the automated detection and classification of cracks in ancient monuments, integrating machine learning and advanced image processing techniques. Our method addresses the pressing need for efficient and objective assessment of structural integrity in these invaluable artifacts. The proposed algorithm begins with preprocessing steps, including image enhancement using adaptive histogram equalization to improve crack visibility. Subsequently, feature extraction techniques such as Grey Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are applied to capture essential characteristics of crack patterns. Central to our approach are the Back Propagation Neural Network (BPNN) and Improved Support Vector Machine (ISVM) classifiers, which are trained on the extracted features to detect and classify cracks with high accuracy. The BPNN learns complex relationships between input features and crack types, while the ISVM leverages a margin-based approach for robust classification. Through extensive experimentation on a diverse dataset of ancient monuments, we demonstrate the effectiveness of our approach in accurately identifying and categorizing cracks. The proposed method offers a scalable and objective solution for monitoring the structural health of ancient monuments, contributing to proactive conservation efforts and the preservation of cultural heritage.
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Navarro and Perez. "Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing." Applied Sciences 9, no. 15 (2019): 3130. http://dx.doi.org/10.3390/app9153130.
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Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.
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Chen, Yunliang, Shaoqian Chen, Nian Zhang, Hao Liu, Honglei Jing, and Geyong Min. "LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks." Complexity 2021 (February 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/8867190.
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The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status of the power grid sensor network. The LPR-MLP model consists of two parts: (1) local binary pattern (LBP), principal component analysis (PCA), and ReliefF are used to process image data and meteorological and mechanical data and (2) the multilayer perceptron (MLP) method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods.
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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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JIAN-PING, LI. "TEXTURE CATEGORIZATION WITH BIOLOGICALLY INSPIRED FEATURES AND RANDOM FORESTS." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 10 (2017): 1–5. https://doi.org/10.5281/zenodo.1456383.
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<strong><strong> </strong>Texture classification is used extensively in computer vision application and images analysis. The aim of this paper was to use biologically inspired mechanisms for features extraction and random forests as a classifier to enhance the texture classification. These mechanisms were implemented by multi channels Gabor filter and multi - scale difference of Gaussian,which were combined efficiently using local binary pattern histograms. These histograms were computedin none overlapped window and classified by ensemble random forests. The experiments results demonstrate that the proposed method improves classification rates. The proposed method achieves higher classification rates compared to other methods.</strong> <strong>https://www.ijiert.org/paper-details?paper_id=141108</strong>
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Palpandi, S., and T. Meera Devi. "Flexible Kernel-Based Fuzzy Means Based Segmentation and Patch-Local Binary Patterns Feature Based Classification System Skin Cancer Detection." Journal of Medical Imaging and Health Informatics 10, no. 11 (2020): 2600–2608. http://dx.doi.org/10.1166/jmihi.2020.3305.
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The high death rates are occurred due to the Melanoma among the skin tumor persons. Melanoma is more dangerous when it raises inside of the skin layer. Hence, watch the wound in depth of the skin is a significant cause to identify melanoma. A (NI) non-invasive computerized dermoscopic (DS) method is introduced in these study. Existing DS system many faces various challenges includes segmentation and classification for detecting the skin cancer. The objective of the research work to improve the segmentation and classification performance. In DS images hair removal and segmentation are performed by using Hybrid Laplacian of Gaussian (HLOG) filter and Flexible Kernel-Based Fuzzy Means (FKFCM), whereas Patch-Local binary patterns (LBP) for feature extraction. The extensive experiment are conducted on largest publicly available benchmark dataset such as PH2, Kaggel and HAM 10000. To validate the performance of proposed technique when compared with traditional segmentation and classification techniques. The proposed system archive 97% of accuracy, 98% sensitivity and 96% of specificity for PH2 dataset. The planned scheme stands out among the few modern literary sources presented in the context of the analysis of DS images in terms of productivity and accepted methodologies, which proves the reliability of the novel study.
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Einy, Sajad, Hasan Saygin, Hemrah Hivehch, and Yahya Dorostkar Navaei. "Local and Deep Features Based Convolutional Neural Network Frameworks for Brain MRI Anomaly Detection." Complexity 2022 (May 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/3081748.
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A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer-aided systems for the diagnosis of brain tumors. In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features for brain MRI images anomaly detection. The first proposed system is Directional Bit-Planes Deep Autoencoder (DBP-DAE) which extracts and learns local and direction features. The DBP-DAE by decomposition of a local binary pattern (LBP) into eight bit-planes extracts are directional and inherent local-structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network (DSRCN) which extracts high (deep) and low-level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP-DAE and DSRCN in an end-to-end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state-of-the-art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s.
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Liu, Chang, Zhuocheng Zou, Yuan Miao, and Jun Qiu. "Light field quality assessment based on aggregation learning of multiple visual features." Optics Express 30, no. 21 (2022): 38298. http://dx.doi.org/10.1364/oe.467754.
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Light field imaging is a way to represent human vision from a computational perspective. It contains more visual information than traditional imaging systems. As a basic problem of light field imaging, light field quality assessment has received extensive attention in recent years. In this study, we explore the characteristics of light field data for different visual domains (spatial, angular, coupled, projection, and depth), study the multiple visual features of a light field, and propose a non-reference light field quality assessment method based on aggregation learning of multiple visual features. The proposed method has four key modules: multi-visual representation of a light field, feature extraction, feature aggregation, and quality assessment. It first extracts the natural scene statistics (NSS) features from the central view image in the spatial domain. It extracts gray-level co-occurrence matrix (GLCM) features both in the angular domain and in the spatial-angular coupled domain. Then, it extracts the rotation-invariant uniform local binary pattern (LBP) features of depth map in the depth domain, and the statistical characteristics of the local entropy (SDLE) features of refocused images in the projection domain. Finally, the multiple visual features are aggregated to form a visual feature vector for the light field. A prediction model is trained by support vector machines (SVM) to establish a light field quality assessment method based on aggregation learning of multiple visual features.
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Hu, Yongsheng, and Liyi Zhang. "MRI-only Radiation Therapy: Pseudo-CT Based on Cubic-Feature Extraction and Alternative Regression Forest." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 14 (2020): 2054033. http://dx.doi.org/10.1142/s0218001420540336.
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Despite the extensive attention attracted by magnetic resonance imaging (MRI) in the radiation therapy, computed tomography was reintroduced by the researchers. During the calculation process of the 3D dose distribution of tissues, there were some arguments about the electron density information obtained from the CT scan. However, the CT-provided bones are accurate for constructing a radiograph. Recently, the advantages boosted by the soft tissue contrast relying on MRI and as well as the advantages boosted by CT imaging have been combined by the using of MRI/CT. Unfortunately, disadvantages still exist in the MRI/CT workflow because the voxel-intensities are unbalanced in the MRI and the CT scan. Here, based on the mapping method of CT and MRI, the potential of pseudo-CT (PCT) instead of CT planning was studied. The estimated PCT only from the corresponding MRI was obtained by using the patch-based random forest regression. The CT voxel target was trained by 3D Gabor feature in the MRI cube and the Local Binary Pattern (LBP). Besides, the regression task was solved by the alternative regression forest. According to the experiment, the method performs better than the current dictionary learning-based (DLB) method or atlas-based (AB) method.
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Anwar, Inzamam, and Naeem Ul Islam. "Learned Features are Better for Ethnicity Classification." Cybernetics and Information Technologies 17, no. 3 (2017): 152–64. http://dx.doi.org/10.1515/cait-2017-0036.
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Abstract Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.
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Dahmouni, Abdellatif, Abdelkaher Ait Abdelouahad, Yasser Aderghal, Ibrahim Guelzim, Insaf Bellamine, and Hassan Silkan. "A Robust Approach for Ulcer Classification/Detection in WCE Images." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 06 (2024): 86–102. http://dx.doi.org/10.3991/ijoe.v20i06.45773.
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Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.
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Sengupta, Jewel, and Robertas Alzbutas. "Intracranial Hemorrhages Segmentation and Features Selection Applying Cuckoo Search Algorithm with Gated Recurrent Unit." Applied Sciences 12, no. 21 (2022): 10851. http://dx.doi.org/10.3390/app122110851.
Full textAbstract:
Generally, traumatic and aneurysmal brain injuries cause intracranial hemorrhages, which is a severe disease that results in death, if it is not treated and diagnosed properly at the early stage. Compared to other imaging techniques, Computed Tomography (CT) images are extensively utilized by clinicians for locating and identifying intracranial hemorrhage regions. However, it is a time-consuming and complex task, which majorly depends on professional clinicians. To highlight this problem, a novel model is developed for the automatic detection of intracranial hemorrhages. After collecting the 3D CT scans from the Radiological Society of North America (RSNA) 2019 brain CT hemorrhage database, the image segmentation is carried out using Fuzzy C Means (FCM) clustering algorithm. Then, the hybrid feature extraction is accomplished on the segmented regions utilizing the Histogram of Oriented Gradients (HoG), Local Ternary Pattern (LTP), and Local Binary Pattern (LBP) to extract discriminative features. Furthermore, the Cuckoo Search Optimization (CSO) algorithm and the Optimized Gated Recurrent Unit (OGRU) classifier are integrated for feature selection and sub-type classification of intracranial hemorrhages. In the resulting segment, the proposed ORGU-CSO model obtained 99.36% of classification accuracy, which is higher related to other considered classifiers.