Journal articles: 'Local texture descriptor'

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Goyal, Aparna, and Reena Gunjan. "Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review." E3S Web of Conferences 170 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202017003007.

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Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.

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Song, Ke Chen, and Yun Hui Yan. "Neighborhood Estimated Local Binary Patterns for Texture Classification." Applied Mechanics and Materials 513-517 (February 2014): 4401–6. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4401.

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A novel texture classification approach based on neighborhood estimated local binary patterns (NELBP) is proposed. In the proposed approach, the local surrounding values of neighborhood estimated are introduced to operate binary patterns. Moreover, two different and complementary descriptors (average-based descriptor and differences-based descriptor) are extracted from local patches. Contrast experiments on Outex database and CUReT database demonstrate that the proposed NELBP is more robust to Gaussian noise than the conventional LBP for texture classification. In addition, the results also show that the combined complementary descriptor playes an important role in texture classification.

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Zeng, Hui, Rui Zhang, Mingming Huang, and Xiuqing Wang. "Compact Local Directional Texture Pattern for Local Image Description." Advances in Multimedia 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/360186.

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This paper presents an effective local image feature region descriptor, called CLDTP descriptor (Compact Local Directional Texture Pattern), and its application in image matching and object recognition. The CLDTP descriptor encodes the directional and contrast information in a local region, so it contains the gradient orientation information and the gradient magnitude information. As the dimension of the CLDTP histogram is much lower than the dimension of the LDTP histogram, the CLDTP descriptor has higher computational efficiency and it is suitable for image matching. Extensive experiments have validated the effectiveness of the designed CLDTP descriptor.

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Suruliandi, A., G. Murugeswari, and P. Arockia Jansi Rani. "Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images." International Journal of Image and Graphics 15, no. 01 (January 2015): 1550001. http://dx.doi.org/10.1142/s0219467815500011.

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Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

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Ramírez Rivera, Adín, Jorge Rojas Castillo, and Oksam Chae. "Local Directional Texture Pattern image descriptor." Pattern Recognition Letters 51 (January 2015): 94–100. http://dx.doi.org/10.1016/j.patrec.2014.08.012.

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Günay, Asuman, and Vasif V. Nabiyev. "Facial Age Estimation Using Spatial Weber Local Descriptor." International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems 6, no. 3 (October 30, 2017): 108. http://dx.doi.org/10.11601/ijates.v6i3.218.

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This paper introduces a novel age estimation method using a new texture descriptor Weber Local Descriptor (WLD). This texture descriptor is analyzed in depth for age estimation problem. In the study, the multi-scale versions of holistic and spatial WLD (SWLD) descriptors are used to extract the age related features from normalized facial images. After finding a lower dimensional feature subspace, age estimation is performed using multiple linear regression. In addition the age estimation accuracy of each of the distinct and intersection block used in spatial texture extraction are investigated. Experiments on FGNET, MORPH and PAL databases have shown that similar age estimation performances can be obtained by using more effective blocks in spatial histogram generation. This also provides us to reduce the number of features and computational cost.

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Suruliandi, A., A. Sinduja, and S. P. Raja. "Texture classification using the rotational-invariant local symmetric tetra pattern." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 04 (July 2019): 1950027. http://dx.doi.org/10.1142/s0219691319500279.

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Feature extraction plays a key role in pattern recognition problems. The texture feature is an important feature which helps to describe an image with textural information. A new texture descriptor, the Local Symmetric Tetra Pattern (LSTP), is proposed in this work. This descriptor is developed for the local description of an image. It considers not only the surrounding eight neighbors, but also the eight pixels at the next level to describe the texture efficiently. For every pixel, the maximum edge value, the number of negative sign bits and the number of positive sign bits for each degree of symmetry are computed. Image classification is experimented using the Original Brodatz, Outex and Kylberg Texture Dataset v.1.0 databases. The investigation results are compared with existing method which shows promising achievement of the proposed techniques in terms of their evaluation measures. It is also found that the proposed texture descriptor is rotationally invariant.

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Arslan, Sibel, and Celal Ozturk. "Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification." Applied Sciences 9, no. 9 (May 10, 2019): 1930. http://dx.doi.org/10.3390/app9091930.

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Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.

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Yan, Shen Hai, Xian Tong Huang, and Yang Liu. "A Novel Texture Spectrum Descriptor." Applied Mechanics and Materials 397-400 (September 2013): 1494–99. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1494.

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A concept of equivalence classes of texture pattern is put forward according to the visual consistency between the rotation texture and the flip texture. An improved texture spectrum descriptor (iTS) is proposed based on the equivalence classes. The iTS depicts the grayscale variation pattern of the pixels in the image neighbour domain and denotes the texture content of an image with a histogram of texture spectrum. Compared with the basic texture spectrum descriptor (TS), local binary pattern (LBP) and Shis local binary pattern (sLBP), iTS has best precision in the image retrieval experiments. The iTS has stronger ability to describle the texture and more adapt to the image rotation transformation.

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El khadiri, I., A. Chahi, Y. El merabet, Y. Ruichek, and R. Touahni. "Local directional ternary pattern: A New texture descriptor for texture classification." Computer Vision and Image Understanding 169 (April 2018): 14–27. http://dx.doi.org/10.1016/j.cviu.2018.01.004.

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Wu, Xiao Sheng, and Jun Ding Sun. "A Brief Study on a Novel Texture Spectrum Descriptor for Material Images." Applied Mechanics and Materials 63-64 (June 2011): 507–10. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.507.

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The local binary pattern (LBP) operator has been proved to be theoretically simple yet very effective for texture description. However, it lacks the full description of texture in a region and produces a rather long histogram. A novel texture spectrum descriptor was proposed to alleviate these limitations in the paper. It uses the relation of 3 pixels in an 8-neighborhood, the center and the center-symmetric pixels, to define the local texture patterns. The new operator fully uses the texture information contained in the 8-neighbourhood and produces a rather short histogram. On the other hand, the new operator also has the same desirable properties as LBP, such as tolerance to illumination changes and computational simplicity. Experimental results demonstrate that the new descriptor achieves better performance than the conventional LBP with a rather short histogram

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Davarpanah, S. Hashem, Fatimah Khalid, Lili Nurliyana Abdullah, and Maryam Golchin. "A texture descriptor: BackGround Local Binary Pattern (BGLBP)." Multimedia Tools and Applications 75, no. 11 (May 10, 2015): 6549–68. http://dx.doi.org/10.1007/s11042-015-2588-3.

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Al Saidi, Ibtissam, Mohammed Rziza, and Johan Debayle. "A Novel Texture Descriptor: Circular Parts Local Binary Pattern." Image Analysis & Stereology 40, no. 2 (July 9, 2021): 105–14. http://dx.doi.org/10.5566/ias.2580.

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Local Binary Pattern (LBP) are considered as a classical descriptor for texture analysis, it has mostly been used in pattern recognition and computer vision applications. However, the LBP gets information from a restricted number of local neighbors which is not enough to describe texture information, and the other descriptors that get a large number of local neighbors suffer from a large dimensionality and consume much time. In this regard, we propose a novel descriptor for texture classification known as Circular Parts Local Binary Pattern (CPLBP) which is designed to enhance LBP by extending the area of neighborhood from one to a region of neighbors using polar coordinates that permit to capture more discriminating relationships that exists amongst the pixels in the local neighborhood which increase efficiency in extracting features. Firstly, the circle is divided into regions with a specific radius and angle. After that, we calculate the average gray-level value of each part. Finally, the value of the center pixel is compared with these average values. The relevance of the proposed idea is validate in databases Outex 10 and 12. A complete evaluation on benchmark data sets reveals CPLBP's high performance. CPLBP generates the score of 99.95 with SVM classification.

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Tikhomirova, T. A., G. T. Fedorenko, K. M. Nazarenko, and E. S. Nazarenko. "LEFT: LOCAL EDGE FEATURES TRANSFORM." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 189 (March 2020): 11–18. http://dx.doi.org/10.14489/vkit.2020.03.pp.011-018.

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To detect point correspondence between images or 3D scenes, local texture descriptors, such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-Up Robust Features), BRIEF (Binary Robust Independent Elementary Features), and others, are usually used. Formally they provide invariance to image rotation and scale, but this properties are achieved only approximately due to discrete number of evaluable orientations and scales stored into the descriptor. Feature points preferable for such descriptors usually are not belong to actual object boundaries into 3D scenes and so are hard to be used into apipolar relationships. At the same time, linking the feature point to large-scale lines and edges is preferable for SLAM (Simultaneous Localization And Mapping) tasks, because their appearance are the most resistible to daily, seasonal and weather variations.In this paper, original feature points descriptor LEFT (Local Edge Features Transform) for edge images are proposed. LEFT accumulate directions and contrasts of alternative strait segments tangent to lines and edges in the vicinity of feature points. Due to this structure, mutual orientation of LEFT descriptors are evaluated and taken into account directly at the stage of their comparison. LEFT descriptors adapt to the shape of contours in the vicinity of feature points, so they can be used to analyze local and global geometric distortions of a various nature. The article presents the results of comparative testing of LEFT and common texture-based descriptors and considers alternative ways of representing them in a computer vision system.

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Ganesan, L., C. Umarani, M. Kaliappan, S. Vimal, Seifedine Kadry, and Yunyoung Nam. "Texture Image Analysis for Larger Lattice Structure using Orthogonal Polynomial framework." Information Technology and Control 51, no. 3 (September 23, 2022): 531–44. http://dx.doi.org/10.5755/j01.itc.51.3.29322.

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An Orthogonal Polynomial Framework using 3 x 3 mathematical model has been proposed and attempted for the textureanalysis by L.Ganesan and P.Bhattacharyya during 1990. They proposed this frame work which was unified to address both edgeand texture detection. Subsequently, this work has been extended for different applications by them and by different authors overa period of time. Now the Orthogonal Polynomial Framework has been shown effective for larger grid size of (5 x 5) or (7 x 7) orhigher, to analyze textured surfaces. The image region (5 x 5) under consideration is evaluated to be textured or untextured usinga statistical approach. Once the image region is concluded to be textured, it is proposed to be described by a local descriptor,called pro5num, computed by a simple coding scheme on the individual pixels based on their computed significant variances. Thehistogram of all the pro5nums computed over the entire image, called pro5spectrum, is considered to be the global descriptor.The novelty of this scheme is that it can be used for discriminating the region under consideration is micro or macro texture,based on the range of values in the global descriptor. This method works fine for many standard texture images. The works usingthe proposed descriptors for many texture analysis problems with (5 x5) including higher grid size and applications are underprogress

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Chen, Xi, Zaihong Zhou, Jiashu Zhang, Zengli Liu, and Qingsong Huang. "Local convex-and-concave pattern: An effective texture descriptor." Information Sciences 363 (October 2016): 120–39. http://dx.doi.org/10.1016/j.ins.2016.05.017.

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Joshi, Sharad, and Nitin Khanna. "Source Printer Classification Using Printer Specific Local Texture Descriptor." IEEE Transactions on Information Forensics and Security 15 (2020): 160–71. http://dx.doi.org/10.1109/tifs.2019.2919869.

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Sandid, Faten, and Ali Douik. "Texture descriptor based on local combination adaptive ternary pattern." IET Image Processing 9, no. 8 (August 1, 2015): 634–42. http://dx.doi.org/10.1049/iet-ipr.2014.0895.

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Roy, Swalpa Kumar, Bhabatosh Chanda, Bidyut B. Chaudhuri, Dipak Kumar Ghosh, and Shiv Ram Dubey. "Local jet pattern: a robust descriptor for texture classification." Multimedia Tools and Applications 79, no. 7-8 (August 27, 2018): 4783–809. http://dx.doi.org/10.1007/s11042-018-6559-3.

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S. Sathiya, Devi. "Texture classification with modified rotation invariant local binary pattern and gradient boosting." International Journal of Knowledge-based and Intelligent Engineering Systems 26, no. 2 (September 29, 2022): 125–36. http://dx.doi.org/10.3233/kes220012.

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Since texture is prominent low level feature of an image, most of the image processing and computer vision applications rely on this feature for efficient extraction, retrieval, visualization and classification of the images. Hence, the texture analysis method mainly concentrates on efficient feature extraction and representation of the image. The images captured and analyzed in many of the applications are not in same (or) similar scale, orientation and illumination and also texture has regular, stochastic, periodic, homogeneous (or) inhomogeneous and directional in nature. To address these issues, recent texture analysis method focused on efficient and invariant feature extraction and representation with reduced dimension. Hence this paper proposes a invariant texture descriptor, Locality preserving Rotation Invariant Modified Directional Local Binary Pattern (LRIMDLBP) based on LBP. The classical LBP descriptor is widely used in most of the texture analysis applications due to its simplicity and robustness to illumination changes. However, it does not efficiently capture the discriminative texture information because it uses sign information and ignores the magnitude value of the neighborhood and also suffers from high dimensionality. Hence to improve the performance of LBP, many variants are proposed. Though most of these LBP variants are either geometrical or direction invariant, fails to address the spatial locality and contrast invariance. To address these issues, the proposed LRIMDLBP incorporates spatial locality, contrast and direction information for rotation invariant texture descriptor with reduced dimension. The proposed LRIMDLBP consists of 5 phases: (i) Reference point identification, (ii) Magnitude calculation, (iii) Binary Label computation based on threshold, (iv) Pattern identification in dominant direction and (v) LRIMDLBP code computation. The locality and rotation invariance is achieved by identifying and using reference point in a local neighborhood. The reference point is a dominant pixel whose magnitude is large in the neighborhood excluding center pixel. The spatial locality and rotation invariance is achieved by preserving the weights of LBP dynamically based on the reference point. The proposed method also preserves the direction information of the texture by comparing the magnitude of the pixel in the four dominant directions such as horizontal, vertical, diagonal and anti-diagonal directions. Finally the proposed invariant LRIMDLBP descriptor computes histogram based on decimal pattern value. The proposed LRIMDLBP descriptor results in texture feature with reduced dimension when compared to other LBP variants. The performance of the proposed descriptor is evaluated with large and well known four bench mark texture datasets namely (i) CUReT, (ii) Outex, (iii) KTS-TIPS and (iv) UIUC against three classifiers such as (i). K-Nearest Neighbor (K-NN), (ii). Support Vector Machine (SVM) with Radial Basis Function (RBF) and (iii). Gradient Boosting Classifier (GBC). The intensive experimental result shows that the ensemble based GBC yields superior classification accuracy of 99.38%, 99.43%, 98.67% and 98.82% for the datasets CUReT, Outex, KTH-TIPS and UIUC respectively when compared with other two classifiers and also improves the generalization ability. The proposed LRIMDLBP descriptor achieves approximately 15% more classification accuracy when compared with traditional LBP and also produces 1% to 2.5% more classification accuracy compared with other state of the art LBP variants.

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Cvetković, Stevica, Nemanja Grujić, Slobodan Ilić, and Goran Stančić. "DETECTION OF TEXTURE-LESS OBJECTS BY LINE-BASED APPROACH." Facta Universitatis, Series: Automatic Control and Robotics 18, no. 2 (January 27, 2020): 079. http://dx.doi.org/10.22190/fuacr1902079c.

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This paper proposes a method for tackling the problem of scalable object instance detection in the presence of clutter and occlusions. It gathers together advantages in respect of the state-of-the-art object detection approaches, being at the same time able to scale favorably with the number of models, computationally efficient and suited to texture-less objects as well. The proposed method has the following advantages: a) generality – it works for both texture-less and textured objects, b) scalability – it scales sub-linearly with the number of objects stored in the object database, and c) computational efficiency – it runs in near real-time. In contrast to the traditional affine-invariant detectors/descriptors which are local and not discriminative for texture-less objects, our method is based on line segments around which it computes semi-global descriptor by encoding gradient information in scale and rotation invariant manner. It relies on both texture and shape information and is, therefore, suited for both textured and texture-less objects. The descriptor is integrated into efficient object detection procedure which exploits the fact that the line segment determines scale, orientation and position of an object, by its two endpoints. This is used to construct several effective techniques for object hypotheses generation, scoring and multiple object reasoning; which are integrated in the proposed object detection procedure. Thanks to its ability to detect objects even if only one correct line match is found, our method allows detection of the objects under heavy clutter and occlusions. Extensive evaluation on several public benchmark datasets for texture-less and textured object detection, demonstrates its scalability and high effectiveness.

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Rassem, Taha H., Nasrin M. Makbol, and Sam Yin Yee. "Face Recognition Using Completed Local Ternary Pattern (CLTP) Texture Descriptor." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (June 1, 2017): 1594. http://dx.doi.org/10.11591/ijece.v7i3.pp1594-1601.

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Nowadays, face recognition becomes one of the important topics in the computer vision and image processing area. This is due to its importance where can be used in many applications. The main key in the face recognition is how to extract distinguishable features from the image to perform high recognition accuracy. Local binary pattern (LBP) and many of its variants used as texture features in many of face recognition systems. Although LBP performed well in many fields, it is sensitive to noise, and different patterns of LBP may classify into the same class that reduces its discriminating property. Completed Local Ternary Pattern (CLTP) is one of the new proposed texture features to overcome the drawbacks of the LBP. The CLTP outperformed LBP and some of its variants in many fields such as texture, scene, and event image classification. In this study, we study and investigate the performance of CLTP operator for face recognition task. The Japanese Female Facial Expression (JAFFE), and FEI face databases are used in the experiments. In the experimental results, CLTP outperformed some previous texture descriptors and achieves higher classification rate for face recognition task which has reached up 99.38% and 85.22% in JAFFE and FEI, respectively.

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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 (January 14, 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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I.Jeena Jacob, Dr, Dr K.G.Srinivasagan, Dr S. Gomathi, Ms Joyce Beryl Princess, Ms P.Betty, and Mr P.Ebby Darney. "Tri-Chrominance Texture Pattern: A New Feature Descriptor." International Journal of Engineering & Technology 7, no. 2.22 (April 20, 2018): 15. http://dx.doi.org/10.14419/ijet.v7i2.22.11801.

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Feature extraction plays a vital role in the information management system. This paper proposes Tri-Chrominance Texture Pattern (TCTP), a feature descriptor for extracting the features from images. This pattern helps to extract the inter-channel chrominance relationship, along with texture information of the image. The analysis were done in a natural image dataset, Corel database (DB1), pure colored texture database, Colored Brodaz Texture database (DB2) and a biometric dataset, Indian Face Image database (DB3). The proposed work outperforms the existing works in all the datasets. The analysis on DB1 shows significant improvement over the previous works like Local Binary Pattern (LBP) (78.64%/57.35%), Local Tetra Pattern (LTrP) (79.84%/56.8%) and Local Oppugnant Color Texture Pattern (LOCTP) (82.64%/58%) as 83.25%/58.2% in terms of Average Precision/ Average Recall. The analysis made in the Colored Brodaz database (DB1) shows the result of TCTP as improved from LBP (91.75%/75.18%), LTrP (91.64%/76%) and LOCTP (99.21%/89.38%) to (99.8%/93.47%). The Average Recognition Rate (ARR) of face recognition in DB3 database using the proposed work shows considerable improvement from LBP (78.2%), LTrP (91.9%) and LOCTP (89.1%) as 88.5%. The computational complexity of the proposed work is much lesser than LTrP and LOCTP.

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FERRAZ, CAROLINA TOLEDO, OSMANDO PEREIRA, MARCOS VERDINI ROSA, and ADILSON GONZAGA. "OBJECT RECOGNITION BASED ON BAG OF FEATURES AND A NEW LOCAL PATTERN DESCRIPTOR." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 08 (December 2014): 1455010. http://dx.doi.org/10.1142/s0218001414550106.

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Bag of Features (BoF) has gained a lot of interest in computer vision. Visual codebook based on robust appearance descriptors extracted from local image patches is an effective means of texture analysis and scene classification. This paper presents a new method for local feature description based on gray-level difference mapping called Mean Local Mapped Pattern (M-LMP). The proposed descriptor is robust to image scaling, rotation, illumination and partial viewpoint changes. The training set is composed of rotated and scaled images, with changes in illumination and view points. The test set is composed of rotated and scaled images. The proposed descriptor more effectively captures smaller differences of the image pixels than similar ones. In our experiments, we implemented an object recognition system based on the M-LMP and compared our results to the Center-Symmetric Local Binary Pattern (CS-LBP) and the Scale-Invariant Feature Transform (SIFT). The results for object classification were analyzed in a BoF methodology and show that our descriptor performs better compared to these two previously published methods.

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García-Olalla, Óscar, Laura Fernández-Robles, Enrique Alegre, Manuel Castejón-Limas, and Eduardo Fidalgo. "Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences." Sensors 19, no. 5 (March 1, 2019): 1048. http://dx.doi.org/10.3390/s19051048.

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This paper presents a new texture descriptor booster, Complete Local Oriented Statistical Information Booster (CLOSIB), based on statistical information of the image. Our proposal uses the statistical information of the texture provided by the image gray-levels differences to increase the discriminative capability of Local Binary Patterns (LBP)-based and other texture descriptors. We demonstrated that Half-CLOSIB and M-CLOSIB versions are more efficient and precise than the general one. H-CLOSIB may eliminate redundant statistical information and the multi-scale version, M-CLOSIB, is more robust. We evaluated our method using four datasets: KTH TIPS (2-a) for material recognition, UIUC and USPTex for general texture recognition and JAFFE for face recognition. The results show that when we combine CLOSIB with well-known LBP-based descriptors, the hit rate increases in all the cases, introducing in this way the idea that CLOSIB can be used to enhance the description of texture in a significant number of situations. Additionally, a comparison with recent algorithms demonstrates that a combination of LBP methods with CLOSIB variants obtains comparable results to those of the state-of-the-art.

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Arun Kumar H. D. and Prabhakar C. J. "Moving Vehicles Detection in Traffic Video Using Modified SXCS-LBP Texture Descriptor." International Journal of Computer Vision and Image Processing 5, no. 2 (July 2015): 14–34. http://dx.doi.org/10.4018/ijcvip.2015070102.

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Background modeling and subtraction based method for moving vehicle's detection in traffic video using a novel texture descriptor called as Modified Spatially eXtended Center Symmetric Local Binary Pattern (Modified SXCS-LBP) descriptor. The XCS-LBP texture descriptor is sensitive to noise because in order to generate binary code, the value of center pixel value is used as the threshold directly, and it does not consider temporal motion information. In order to solve this problem, this paper proposed a novel texture descriptor called as Modified SXCS-LBP descriptor for moving vehicle detection based on background modeling and subtraction. The proposed descriptor is robust against noise, illumination variation, and able to detect slow moving vehicles because it considers both spatial and temporal moving information. The evaluation carried out using precision and recall metric, which are obtained using experiments conducted on two popular datasets such as BMC and CDnet datasets. The experimental result shows that the authors' method outperforms existing texture and non-texture based methods.

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Rose, R. Reena, A. Suruliandi, and K. Meena. "Local texture description framework-based modified local directional number pattern: a new descriptor for face recognition." International Journal of Biometrics 7, no. 2 (2015): 147. http://dx.doi.org/10.1504/ijbm.2015.070928.

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Huang, Weijia, Weixing Zhu, Changhua Ma, and Yizheng Guo. "Weber Texture Local Descriptor for Identification of Group-Housed Pigs." Sensors 20, no. 16 (August 18, 2020): 4649. http://dx.doi.org/10.3390/s20164649.

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The individual identification of group-housed pigs plays an important role in breeding process management and individual behavior analysis. Recently, livestock identification methods based on the side view or face image have strict requirements on the position and posture of livestock, which poses a challenge for the application of the monitoring scene of group-housed pigs. To address the issue above, a Weber texture local descriptor (WTLD) is proposed for the identification of group-housed pigs by extracting the local features of back hair, skin texture, spots, and so on. By calculating the differential excitation and multi-directional information of pixels, the local structure features of the main direction are fused to enhance the description ability of features. The experimental results show that the proposed WTLD achieves higher recognition rates with a lower feature dimension. This method can identify pig individuals with different positions and postures in the pig house. Without limitations on pig movement, this method can facilitate the identification of individual pigs with greater convenience and universality.

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Yu, Laihang, Lin Feng, Huibing Wang, Li Li, Yang Liu, and Shenglan Liu. "Multi-trend binary code descriptor: a novel local texture feature descriptor for image retrieval." Signal, Image and Video Processing 12, no. 2 (July 29, 2017): 247–54. http://dx.doi.org/10.1007/s11760-017-1152-1.

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WANG, Kangru, Lei QU, Lili CHEN, Jiamao LI, Yuzhang GU, Dongchen ZHU, and Xiaolin ZHANG. "Ground Plane Detection with a New Local Disparity Texture Descriptor." IEICE Transactions on Information and Systems E100.D, no. 10 (2017): 2664–68. http://dx.doi.org/10.1587/transinf.2017edl8053.

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Jiangping He, Hongwei Ji, and Xin Yang. "Rotation Invariant Texture Descriptor Using Local Shearlet-Based Energy Histograms." IEEE Signal Processing Letters 20, no. 9 (September 2013): 905–8. http://dx.doi.org/10.1109/lsp.2013.2267730.

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Qi, Xianbiao, Guoying Zhao, Linlin Shen, Qingquan Li, and Matti Pietikäinen. "LOAD: Local orientation adaptive descriptor for texture and material classification." Neurocomputing 184 (April 2016): 28–35. http://dx.doi.org/10.1016/j.neucom.2015.07.142.

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Kaddar, Bachir, Hadria Fizazi, and Abdel-Ouahab Boudraa. "Texture features based on an efficient local binary pattern descriptor." Computers & Electrical Engineering 70 (August 2018): 496–508. http://dx.doi.org/10.1016/j.compeleceng.2017.08.009.

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Li, Jing, Nong Sang, and Changxin Gao. "LEDTD: Local edge direction and texture descriptor for face recognition." Signal Processing: Image Communication 41 (February 2016): 40–45. http://dx.doi.org/10.1016/j.image.2015.12.003.

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Ahmed, Faisal, and Emam Hossain. "Automated Facial Expression Recognition Using Gradient-Based Ternary Texture Patterns." Chinese Journal of Engineering 2013 (December 25, 2013): 1–8. http://dx.doi.org/10.1155/2013/831747.

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Recognition of human expression from facial image is an interesting research area, which has received increasing attention in the recent years. A robust and effective facial feature descriptor is the key to designing a successful expression recognition system. Although much progress has been made, deriving a face feature descriptor that can perform consistently under changing environment is still a difficult and challenging task. In this paper, we present the gradient local ternary pattern (GLTP)—a discriminative local texture feature for representing facial expression. The proposed GLTP operator encodes the local texture of an image by computing the gradient magnitudes of the local neighborhood and quantizing those values in three discrimination levels. The location and occurrence information of the resulting micropatterns is then used as the face feature descriptor. The performance of the proposed method has been evaluated for the person-independent face expression recognition task. Experiments with prototypic expression images from the Cohn-Kanade (CK) face expression database validate that the GLTP feature descriptor can effectively encode the facial texture and thus achieves improved recognition performance than some well-known appearance-based facial features.

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Sinduja, A., A. Suruliandi, and S. P. Raja. "Empirical Evaluation of Texture Features and Classifiers for Liver Disease Diagnosis." International Journal of Image and Graphics 20, no. 02 (April 2020): 2050015. http://dx.doi.org/10.1142/s0219467820500151.

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The liver cancer is one of the most common fatal diseases worldwide, and its early detection through medical imaging is a major contributor to the reduction in mortality from certain cancer. This paves the way to work on diagnosing liver diseases effectively. An accurate diagnosis of liver disease in CT image requires an efficient description of textures and classification methods. This paper performs comparative analysis on proposed texture feature descriptor with the different existing texture features with various classifiers to classify six types of diffused and focal liver diseases. The classification of liver diseases is done in two stages. In first stage, features like segmentation based fractal texture analysis, counting label occurrence matrix, local configuration pattern, eXtended center-symmetric local binary pattern and the proposed local symmetric tetra pattern are used for extracting information from the CT liver structure and classifiers like support vector machine, [Formula: see text]-nearest neighbor, and naive Bayes are used for classifying the pathologic liver. When pathologic conditions are detected, the best feature descriptors and classifiers are used to classify the results into any of six exclusive pathologic liver diseases, in second stage. The experiments are carried out in medically validated liver datasets containing normal and six-disease category of liver. The first experiment is analyzed using sensitivity, specificity, and accuracy. The second experiment is evaluated using precision, recall, BCR, and F-measure. The results demonstrate that the local symmetric tetra pattern with [Formula: see text]-nearest neighbor classifier culminates in a state-of-the-art performance for diagnosing liver diseases.

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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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LIU, HONGMIN, SHANSHAN ZHI, and ZHIHENG WANG. "IOCD: INTENSITY ORDER CURVE DESCRIPTOR." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 07 (November 2013): 1355011. http://dx.doi.org/10.1142/s0218001413550112.

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Curve matching plays an important role in pattern recognition, computer vision and image understanding. In several past years, this problem has been studied mainly based on the curve contour, while only little progress has been made using the texture feature of the curve's neighborhood. This paper develops a novel texture-based curve matching method called IOCD, which consists of three steps: (1) Curve support region (CSR) without assigning a dominant orientation is first determined; (2) CSR is equally partitioned into several order bins according to the overall intensity order; (3) The feature vector is computed based on the local intensity order mapping. Experiments prove that the proposed IOCD performs robust to image rotation, viewpoint change, illumination change, blur, noise and JPEG compress. The application of image mosaic further identifies IOCD can achieve good matching performance.

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Chelladurai, Callins Christiyana, and Rajamani Vayanaperumal. "Weber Global Statistics Tri- Directional Pattern (WGSTriDP): A Texture Feature Descriptor for Image Retrieval." Information Technology and Control 51, no. 3 (September 23, 2022): 515–30. http://dx.doi.org/10.5755/j01.itc.51.3.30795.

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The texture is a high-flying feature in an image and has been extracted to represent the image for image retrieval applications. Many texture features are being offered for image retrieval. This paper proposes a local binary pattern-based texture feature called Weber Global Statistics Tri-Directional Pattern (WGSTriDP) to retrieve the images. This pattern combines the advantages of differential excitation components in the Weber Local Binary Pattern (WLBP), sign and magnitude components in the Local Tri-Directional Pattern (LTriDP), and global statistics. Differential Excitation (DE) and Global Statistics TriDirectional Pattern (GSTriDP) are two components of WGSTriDP. The WGSTriDP gains the benefit of discrimination concerning human perception from differential excitation as well as incorporates global statistics into sign and magnitude components in the pattern derived from the local neighborhoods. The effectiveness of the pattern in image retrieval is experimented with in two benchmark databases, such as ORL (face database) and UIUC (texture database). According to the results of the experiments, WGSTriDP outperforms other local patterns in retrieving similar images from the database.

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Parveen, Sajida, Nadeem Naeem, and Jherna Devi. "REVIEW ON LOCAL BINARY PATTERN (LBP) TEXTURE DESCRIPTOR AND ITS VARIANTS." International Journal of Advanced Research 5, no. 5 (May 31, 2017): 708–17. http://dx.doi.org/10.21474/ijar01/4169.

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Qian, Xueming, Xian-Sheng Hua, Ping Chen, and Liangjun Ke. "PLBP: An effective local binary patterns texture descriptor with pyramid representation." Pattern Recognition 44, no. 10-11 (October 2011): 2502–15. http://dx.doi.org/10.1016/j.patcog.2011.03.029.

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Roy, Swalpa Kumar, Bhabatosh Chanda, Bidyut B. Chaudhuri, Soumitro Banerjee, Dipak Kumar Ghosh, and Shiv Ram Dubey. "Local directional ZigZag pattern: A rotation invariant descriptor for texture classification." Pattern Recognition Letters 108 (June 2018): 23–30. http://dx.doi.org/10.1016/j.patrec.2018.02.027.

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Roy, Swalpa Kumar, Bhabatosh Chanda, Bidyut B. Chaudhuri, Dipak Kumar Ghosh, and Shiv Ram Dubey. "Local morphological pattern: A scale space shape descriptor for texture classification." Digital Signal Processing 82 (November 2018): 152–65. http://dx.doi.org/10.1016/j.dsp.2018.06.016.

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Hussain, Muhammad, Sahar Qasem, George Bebis, Ghulam Muhammad, Hatim Aboalsamh, and Hassan Mathkour. "Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors." International Journal on Artificial Intelligence Tools 24, no. 04 (August 2015): 1540016. http://dx.doi.org/10.1142/s0218213015400163.

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Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.

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TR, Athira, and Abraham Varghese. "CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (March 1, 2017): 8. http://dx.doi.org/10.11591/ijai.v6.i1.pp8-17.

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Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.

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Tong, Ying, and Rui Chen. "Local Dominant Directional Symmetrical Coding Patterns for Facial Expression Recognition." Computational Intelligence and Neuroscience 2019 (May 13, 2019): 1–13. http://dx.doi.org/10.1155/2019/3587036.

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To overcome the shortcomings of inaccurate textural direction representation and high-computational complexity of Local Binary Patterns (LBPs), we propose a novel feature descriptor named as Local Dominant Directional Symmetrical Coding Patterns (LDDSCPs). Inspired by the directional sensitivity of human visual system, we partition eight convolution masks into two symmetrical groups according to their directions and adopt these two groups to compute the convolution values of each pixel. Then, we encode the dominant direction information of facial expression texture by comparing each pixel’s convolution values with the average strength of its belonging group and obtain LDDSCP-1 and LDDSCP-2 codes, respectively. At last, in view of the symmetry of two groups of direction masks, we stack these corresponding histograms of LDDSCP-1 and LDDSCP-2 codes into the ultimate LDDSCP feature vector which has effects on the more precise facial feature description and the lower computational complexity. Experimental results on the JAFFE and Cohn-Kanade databases demonstrate that the proposed LDDSCP feature descriptor compared with LBP, Gabor, and other traditional operators achieves superior performance in recognition rate and computational complexity. Furthermore, it is also no less inferior to some state-of-the-art local descriptors like as LDP, LDNP, es-LBP, and GDP.

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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 (June 30, 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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Pitchaiyan, Shanthi, and Nickolas Savarimuthu. "Deep Stacked Autoencoder-Based Automatic Emotion Recognition Using an Efficient Hybrid Local Texture Descriptor." Journal of Information Technology Research 15, no. 1 (January 2022): 1–26. http://dx.doi.org/10.4018/jitr.2022010103.

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Extracting an effective facial feature representation is the critical task for an automatic expression recognition system. Local Binary Pattern (LBP) is known to be a popular texture feature for facial expression recognition. However, only a few approaches utilize the relationship between local neighborhood pixels itself. This paper presents a Hybrid Local Texture Descriptor (HLTD) which is derived from the logical fusion of Local Neighborhood XNOR Patterns (LNXP) and LBP to investigate the potential of positional pixel relationship in automatic emotion recognition. The LNXP encodes texture information based on two nearest vertical and/or horizontal neighboring pixel of the current pixel whereas LBP encodes the center pixel relationship of the neighboring pixel. After logical feature fusion, the Deep Stacked Autoencoder (DSA) is established on the CK+, MMI and KDEF-dyn dataset and the results show that the proposed HLTD based approach outperforms many of the state of art methods with an average recognition rate of 97.5% for CK+, 94.1% for MMI and 88.5% for KDEF.

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Li, Haipeng, Ramakrishnan Mukundan, and Shelley Boyd. "Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms." Journal of Imaging 7, no. 10 (October 6, 2021): 205. http://dx.doi.org/10.3390/jimaging7100205.

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This paper investigates the usefulness of multi-fractal analysis and local binary patterns (LBP) as texture descriptors for classifying mammogram images into different breast density categories. Multi-fractal analysis is also used in the pre-processing step to segment the region of interest (ROI). We use four multi-fractal measures and the LBP method to extract texture features, and to compare their classification performance in experiments. In addition, a feature descriptor combining multi-fractal features and multi-resolution LBP (MLBP) features is proposed and evaluated in this study to improve classification accuracy. An autoencoder network and principal component analysis (PCA) are used for reducing feature redundancy in the classification model. A full field digital mammogram (FFDM) dataset, INBreast, which contains 409 mammogram images, is used in our experiment. BI-RADS density labels given by radiologists are used as the ground truth to evaluate the classification results using the proposed methods. Experimental results show that the proposed feature descriptor based on multi-fractal features and LBP result in higher classification accuracy than using individual texture feature sets.

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Journal articles on the topic 'Local texture descriptor'

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