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Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
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“Peng et al. (2019) [89] Structured Light Laser line grid projector images, IHED deep learning model Box volume measurement Measure volumes ranging from 100 mm to 1800 mm with an accuracy of ±5.0 mm”
From Use and Potential of AI in Assisting Surveyors in Building Retrofit and Demolition—A Scoping Review (2025) · cites this paper as [89]
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Research works printed but not linked: 2
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- Yuan, J. and Rao, S. 1993. Spectral estimation for random fields with applications to Markov modeling and texture classification. In Markov Random Fields: Theory and Application, R. Chellappa and A. Jain (Eds.). Academic Press.
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Items 101–200 of 1,001
Oriented edge forests for boundary detection
Hallman, S., & Fowlkes, C. C. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298782 · Cited by 193
“These statistics can be captured in a variety of ways, e.g. via textons [15], sparse coding [22], or measures of self-similarity [14].”
BackgroundSparse distance learning for object recognition combining RGB and depth information
Lai, K., Bo, L., Ren, X., & Fox, D. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980377 · Cited by 191
“Additionally, we extract texton histograms [16] features, which capture texture information using oriented gaussian filter responses.”
MethodAn Investigation of Local Descriptors for Biometric Spoofing Detection
Gragnaniello, D., Poggi, G., Sansone, C., & Verdoliva, L. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2404294 · Cited by 185
“A key step in this direction can be traced back to [63] where, to recognize surfaces made of different materials , a vector of features F( was associated with each pixel x, where each feature is obtained as the output of a specific linear filter.”
Recognizing Materials Using Perceptually Inspired Features
Sharan, L., Liu, C., Rosenholtz, R. E., & Adelson, E. H. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0609-0 · Cited by 189
“1b, surfaces made of different materials can exhibit similar textures, and therefore, systems designed for texture recognition (Leung and Malik 2001; Varma and Zisserman 2009) may not be adequate for material category recognition.”
BackgroundWhen Location Meets Social Multimedia
Ji, R., Gao, Y., Liu, W., Xie, X., Tian, Q. C., & Li, X. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2597181 · Cited by 183
Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images
Zhang, J., Liu, M., An, L., Gao, Y., & Shen, D. G. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2704614 · Cited by 181
“The bag-of-words strategy has demonstrated impressive performance on text, language, and image classification [39], [40], [41], [42].”
BackgroundA Closed-Form Solution to Retinex with Nonlocal Texture Constraints
Zhao, Q., Tan, P., Dai, Q., et al. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.77 · Cited by 179
“Based on the observation that a surface can be described by a small number of local texture structures [13], nonadjacent or distant pixels with the same local texture are assumed to have the same reflectance.”
BackgroundReal-Time Action Recognition With Deeply Transferred Motion Vector CNNs
Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2791180 · Cited by 178
“BoVW framework is originally proposed for image classification [16].”
MethodArea Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification
Wang, L., Pedersen, P. C., Agu, E., Strong, D. M., & Tulu, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2632522 · Cited by 177
“This direction of development is now established as a state-of-the-art approach for visual object category classification [17] [33] [34].”
BackgroundZhao, L., Tang, P. T. P., & Huo, L. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2339842 · Cited by 174
“In recent years, the advent of the bag-of-visual-words (BOVWs) model, which received much concern in the field of image analysis and classification [12]–[18], has provided an efficient means to solve the problem of remote sensing imagebased land-use scene classification.”
BackgroundHung, C., Xu, Z., & Sukkarieh, S. (2014) · Remote Sensing · doi:10.3390/rs61212037 · Cited by 173
“We applied the texton approach to classifying feature responses, as it has been shown to result in a high accuracy for image classification tasks [36,37]”
MethodAutomated Pothole Distress Assessment Using Asphalt Pavement Video Data
Koch, C., Jog, G. M., & Brilakis, I. (2012) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000232 · Cited by 173
Vector Quantizing Feature Space with a Regular Lattice
Tuytelaars, T., & Schmid, C. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408924 · Cited by 168
“Many current object recognition systems use as basic components vector-quantized local features, also referred to as ‘textons’ [ 16 ], ‘object parts’ [7], ‘visual words’ [26], or ‘codebooks’ [15].”
BackgroundThe local binary pattern approach to texture analysis — extensions and applications
Mäenpää, T. (2003) · Cited by 168
3D Texture Recognition Using Bidirectional Feature Histograms
Cula, O. G., & Dana, K. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000020670.05764.55 · Cited by 167
“A widely used computational approach for encoding the local structural attributes of textures is based on multichannel filtering (Bovik et al., 1990) (Jain et al., 1999) (Randen and Husoy, 1999) (Leung and Malik, 2001).”
BackgroundMethodInfluentialImprovements of Object Detection Using Boosted Histograms
Ivan Laptev, A. (2006) · British Machine Vision Conference (BMVC) · doi:10.5244/c.20.97 · Cited by 166
“Histograms of Textons were proposed by Leung and Malik [8] for texture recognition.”
MethodLETRIST: Locally Encoded Transform Feature Histogram for Rotation-Invariant Texture Classification
Song, T., Li, H., Meng, F., Wu, Q., & Cai, J. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2671899 · Cited by 166
“The texton-based representations [11], [17], [35], [36] involve a learning process to generate a texton dictionary (i.”
BackgroundTowards Online Iris and Periocular Recognition Under Relaxed Imaging Constraints
Tan, C.-W., & Kumar, A. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2260165 · Cited by 161
“In this paper, several commonly employed feature extraction approaches such as SIFT [49], [50], GIST [47], LBP [52], HoG [53] and LMF (Leung-Malik Filters) [48] were exploited for computing periocular features.”
MethodCompact representation of bidirectional texture functions
Cula, O. G., & Dana, K. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2001.990645 · Cited by 161
TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces
Xie, X., & Mirmehdi, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1038 · Cited by 159
“Although also in a three layer structure, Leung and Malik [15] adopted a discriminative model, where an image was deterministically transformed by filtering through a bank of Gaussian filters at different orientations, scales, and phases.”
BackgroundDeep Texture Manifold for Ground Terrain Recognition
Xue, J., Zhang, H., & Dana, K. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00065 · Cited by 159
“In classic approaches for texture modeling, images are filtered with a set of handcrafted filter banks followed by grouping the outputs into texton histograms [7, 16, 21, 34], or bag-of-words [6, 15].”
MethodLearning and prediction of slip from visual information
Angelova, A., Matthies, L. H., Helmick, D. M., & Perona, P. (2007) · Journal of Field Robotics · doi:10.1002/rob.20179 · Cited by 155
Intrinsic image decomposition with non-local texture cues
Li, S., Tan, P., & Lin, S. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587660 · Cited by 154
“In our work, texture may be represented by any model, such as filter responses [14] or textons [13], but for simplicity we directly represent the texture at a pixel as a vector of concatenated pixel values from its surrounding neighborhood.”
BackgroundNonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis
Sandler, R., & Lindenbaum, M. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.18 · Cited by 153
“are the mosaics large enough to render descriptor distribution methods (e.g., [ 20 ]) effective.”
BackgroundImage-based defect detection in lithium-ion battery electrode using convolutional neural networks
Badmos, O., Kopp, A., Bernthaler, T., & Schneider, G. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01484-x · Cited by 162
A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid
Xu, Y., Yang, X., Ling, H., et al. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540217 · Cited by 152
“There have been extensive studies on texture descriptors robust to various environmental changes.”
BackgroundLocal Energy Pattern for Texture Classification Using Self-Adaptive Quantization Thresholds
Zhang, J., Liang, J., & Zhao, H. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214045 · Cited by 151
“Therefore, texton dictionary-based approaches [3]–[5] need a learning stage to generate the texton dictionary, but the dictionary is mostly defined by a once-and-for-all clustering of local feature vectors from the training dataset [16].”
BackgroundMethodInfluentialLocally Invariant Fractal Features for Statistical Texture Classification
Varma, M., & Garg, R. D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408876 · Cited by 150
“This is a significant shortcoming as textures have often shown to be best described by the statistical distribution of textons [3, 8, 12, 21, 27, 31].”
BackgroundJoint learning of visual attributes, object classes and visual saliency
Wang, G., & Forsyth, D. A. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459194 · Cited by 150
“Material attribute data: In the material data set, we use a texton feature [15] to represent visual attributes.”
MethodSynthesis of bidirectional texture functions on arbitrary surfaces
Tong, X., Zhang, J., Liu, L., et al. (2002) · ACM Transactions on Graphics · doi:10.1145/566654.566634 · Cited by 150
Visualizing and Understanding Deep Texture Representations
Lin, T., & Maji, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.305 · Cited by 150
“These descriptors are inspired by early work on texture representations [6, 24, 30, 19] that were built on top of wavelet coefficients, linear filter bank responses, or SIFT features [27].”
BackgroundMaterial-specific adaptation of color invariant features
Burghouts, G. J., & Geusebroek, J. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.10.005 · Cited by 148
“is to first establish representatives of the filter outputs, or textons (Leung and Malik, 2001).”
BackgroundMethodInfluentialExemplar-Based Color Constancy and Multiple Illumination
Joze, H. R. V., & Drew, M. S. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.169 · Cited by 146
Classification with scattering operators
Bruna, J., & Mallat, S. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995635 · Cited by 151
“For texture analysis, wavelets with a better frequency localization are often used [7].”
MethodGarcia‐Pineda, O., Zimmer, B. G., Howard, M., Pichel, W. G., Li, X., & MacDonald, I. R. (2009) · Canadian Journal of Remote Sensing · doi:10.5589/m09-035 · Cited by 143
“The next level of analysis detected features using texture filters, namely the Leung–Malik filter bank (LMFB) (Leung and Malik, 2001).”
MethodMa, N., Fan, J., Wang, W., Wu, J., Jiang, Y., Xie, L., & Fan, R. (2022) · Transportation Safety and Environment · doi:10.1093/tse/tdac026 · Cited by 159
van Tulder, G., & de Bruijne, M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526687 · Cited by 142
“A surprising difference with our previous results was the improved performance of the Leung-Malik and Schmid filter banks.”
MethodInfluentialScanpath modeling and classification with hidden Markov models
Coutrot, A., Hsiao, J. H., & Chan, A. B. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0876-8 · Cited by 150
“They convolved each image with 48 filters from the Leung-Malik filter bank corresponding to different spatial scales and orientations (Leung & Malik, 2001), and extracted the response of each filter at each eye position.”
MethodUsing Dependent Regions for Object Categorization in a Generative Framework
Wang, G., Zhang, Y., & Fei-Fei, L. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.324 · Cited by 141
“Initial“bag of words” model starts with modeling some signature distributions of textons (or codewords) [15, 4] without any “latent themes”.”
BackgroundLarge-Margin Multi-Modal Deep Learning for RGB-D Object Recognition
Wang, A., Lu, J., Cai, J., Cham, T.-J., & Wang, G. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2476655 · Cited by 153
“SURF [3], texton [34] and color histogram [1] are used to de-”
MethodObjects as Attributes for Scene Classification
Li, L.-J., Su, H., Lim, Y., & Fei-Fei, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35749-7_5 · Cited by 142
“To summarize, a number of papers have focused on using low-level features for image classification, such as GIST [33], filterbanks [14, 34, 27], and Bag of Words of local features [2, 11].”
BackgroundWavelet Domain Multifractal Analysis for Static and Dynamic Texture Classification
Ji, H., Yang, X., Ling, H., & Xu, Y. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214040 · Cited by 138
“For implementation, we use the algorithm introduced in [19] that generates a d-dimensional MFS feature, where d = 26, [α0, αd ] = [1, 4], and the interval [1, 4] is equally divided into 26 subintervals with breakpoints {αi }, i = 0, 1, 2, .”
BackgroundMethodSong, G., Song, K., & Yan, Y. (2020) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.106000 · Cited by 140
MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation
Wu, J., Zhao, Y., Zhu, J.-Y., et al. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.40 · Cited by 139
“We use average color of all pixels in both RGB color space and L*a*b* color space, and Leung-Malik (LM) Filter Bank [21] as features for superpixels.”
MethodDo, V. T., & Chong, U. (2011) · Strojniški vestnik – Journal of Mechanical Engineering · doi:10.5545/sv-jme.2010.162 · Cited by 139
Attribute-augmented semantic hierarchy
Zhang, H., Zha, Z.-J., Yang, Y., Yan, S., Gao, Y., & Chua, T. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502093 · Cited by 139
“Texture descriptors were computed for each pixel as the 48-D responses of texton filter banks [14].”
MethodSaliency Based Ulcer Detection for Wireless Capsule Endoscopy Diagnosis
Yuan, Y., Wang, J., Li, B., & Meng, M. Q. ‐. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2418534 · Cited by 136
“In this paper, texture features of superpixel regions are extracted by using Leung-Malik (LM) filter bank [32].”
MethodA Successive Clutter-Rejection-Based Approach for Early Detection of Diabetic Retinopathy
Ram, K., Joshi, G. D., & Sivaswamy, J. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2096223 · Cited by 137
“A set of oriented filters are obtained by rotating a second-derivative of Gaussian filter [22].”
MethodSatheesha, T. Y., Satyanarayana, D., Prasad, M. N. G., & Dhruve, K. D. (2017) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2017.2648797 · Cited by 131
“In the research work presented here, three different classes of classifiers i.e. SVM [50], [51], AdaBoost [52] and the recently developed bag-of-features (BoF) [53], [54] classifiers are adopted.”
BackgroundMethodIntroduction to the Bag of Features Paradigm for Image Classification and RetrievalPreprint
O′Hara, S., & Draper, B. A. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1101.3354 · Cited by 198
On Affine Invariant Clustering and Automatic Cast Listing in Movies
Fitzgibbon, A. W., & Zisserman, A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47977-5_20 · Cited by 132
“Finally, affine invariant clustering on filter responses will enable viewpoint variant and invariant textons [17] to be learnt from single images of general curved surfaces, instead of, as now, from images of textured planes.”
BackgroundLearning Optimal Seeds for Diffusion-Based Salient Object Detection
Lu, S., Mahadevan, V., & Vasconcelos, N. M. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.357 · Cited by 131
“Local contrast measures [26] based on Chi-square distances between distributions of color and texton response [29] and geometric attributes such as size, and position.”
MethodA Six-Stimulus Theory for Stochastic Texture
Geusebroek, J., & Smeulders, A. W. M. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-4632-7 · Cited by 131
Object Bank: An Object-Level Image Representation for High-Level Visual Recognition
Li, L.-J., Su, H., Lim, Y., & Fei-Fei, L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0660-x · Cited by 131
Valentinitsch, A., Trebeschi, S., Kaesmacher, J., Cristian Lorenz, A., Löffler, M. T., Zimmer, C., Baum, T. H., & Kirschke, J. S. (2019) · Osteoporosis International · doi:10.1007/s00198-019-04910-1 · Cited by 129
Automatic classification of mammographic parenchymal patterns: a statistical approach
Petroudi, S., Kadir, T., & Brady, M. (2004) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2003.1279885 · Cited by 129
Automated essay scoring with string kernels and word embeddings
Cozma, M., Butnaru, A. M., & Ionescu, R. T. (2018) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p18-2080 · Cited by 127
Tree-based Classifiers for Bilayer Video Segmentation
Yin, P., Criminisi, A., Winn, J. M., & Essa, I. A. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383008 · Cited by 124
“The segmentation results are comparable to those obtained by stereo systems.”
BackgroundResultA Statistical Approach to Texture Classification from Single Images
Varma, M., & Zisserman, A. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000046589.39864.ee · Cited by 124
“Our approach is most closely related to those of Leung and Malik (2001), Schmid (2001) and Cula and Dana (2004).”
BackgroundMethodResultInfluentialLocal Higher-Order Statistics (LHS) for Texture Categorization and Facial Analysis
Sharma, G., Hussain, S. U., & Jurie, F. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33786-4_1 · Cited by 122
“Although the community has spent a lot of effort on object-category classification and object segmentation tasks [1] – leading to very powerful intermediate representation of images such as the BoW model [2,3], texture recognition has received relatively less attention despite its importance for several computer visio…”
BackgroundMethodImage Segmentation UsingHigher-Order Correlation Clustering
Kim, S., Yoo, C. D., Nowozin, S., & Kohli, P. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2303095 · Cited by 122
“between the means of two superpixels using 15 Leung-Malik (LM) filter banks [37] and one x(2)-distance and 48 EMDs (from various numbers of bins and thresholds for ground distance) between texture histograms of the two superpixels.”
MethodHistogram of oriented rectangles: A new pose descriptor for human action recognition
İkizler-Cinbiş, N., & Duygulu, P. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.02.002 · Cited by 121
“The distance between frames is computed using v(2) distance between the histograms (as in [35]).”
MethodCombining LBP Difference and Feature Correlation for Texture Description
Hong, X., Zhao, G., Pietikäinen, M., & Chen, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2316640 · Cited by 120
“Images in each of the nine scales are captured under three poses and three illumination conditions, resulting in a total of 9 images per scale, and hence 81 images per material.”
BackgroundTowards Scalable Dataset Construction: An Active Learning Approach
Collins, B. M., Deng, J., Li, K., & Fei-Fei, L. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_8 · Cited by 118
“– Filterbank response histograms: To obtain filterbank response histograms, we first convolve each image with each of 48 kernels, taken from the LM-48 filterbanks [24].”
MethodThe MediaMill TRECVID 2004 Semantic Video Search Engine
Snoek, C. G. M., van de Sande, K. E. A., de Rooij, O., Huurnink, B., van Gemert, J. C., Uijlings, J., He, J., Li, X., Everts, I., Nedović, V., et al. (2004) · UvA-DARE (University of Amsterdam) · Cited by 117
The Remote Sensing and GIS Software Library (RSGISLib)
Bunting, P., Clewley, D., Lucas, R., et al. (2013) · Computers & Geosciences · doi:10.1016/j.cageo.2013.08.007 · Cited by 116
“Within the filtering library is an interface for applying individual filters, or a bank of filters (e.g., the Leung-Malik filter bank; Leung and Malik, 2001) to images.”
BackgroundMethodData-driven image color theme enhancement
Wang, B., Yu, Y., Wong, T., et al. (2010) · ACM Transactions on Graphics · doi:10.1145/1882261.1866172 · Cited by 116
Tokarczyk, P., Wegner, J. D., Walk, S., & Schindler, K. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2321423 · Cited by 112
Scale-space texture description on SIFT-like textons
Xu, Y., Huang, S., Ji, H., & Fermuller, C. M. (2012) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.05.003 · Cited by 114
Bidirectional Texture Function Modeling: A State of the Art Survey
Filip, J., & Haindl, M. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.246 · Cited by 113
“A 3D texton set is constructed using the [57] method (BTF space clustering) and single BTF pixels are assigned texton labels.”
Building and using a semantivisual image hierarchy
Li, L.-J., Wang, C., Lim, Y., Blei, D. M., & Fei-Fei, L. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540027 · Cited by 112
“Example images from each of the 40 Flickr classes. top and bottom position), texture (normalized texton [21] histogram ) and normalized SIFT histogram respectively.”
BackgroundImproving object detection with boosted histograms
Laptev, I. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.010 · Cited by 111
“Histograms of Textons were proposed by Leung and Malik [14] as well as by Varma and Zisserman [27] for texture recognition.”
MethodCategorizing Nine Visual Classes Using Local Appearance Descriptors
Willamowski, J. K., Arregui, D., Csurka, G., Dance, C. R., & Fan, L. (2004) · Cited by 112
Goldfeder, C., & Allen, P. K. (2011) · Autonomous Robots · doi:10.1007/s10514-011-9228-1 · Cited by 111
“We refer the reader to this survey (Landy and Graham 2004) and to the work of Leung and Malik (2001) which focuses specifically on recognizing real world materials from small numbers of photographs.”
MethodSampling Strategies for Real-Time Action Recognition
Shi, F., Petriu, E. M., & Laganière, R. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.335 · Cited by 110
“Dense sampling has shown to produce good results for image classification [1] [15].”
Retrieval and classification of food images
Farinella, G. M., Allegra, D., Moltisanti, M., Stanco, F., & Battiato, S. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.07.006 · Cited by 109
GraB: Visual Saliency via Novel Graph Model and Background Priors
Wang, Q., Zheng, W., & Piramuthu, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.64 · Cited by 107
“For texture features, we use responses from the Leung-Malik (LM) filter bank [18].”
Method3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Cobzaş, D., Birkbeck, N., Schmidt, M., Jägersand, M., & Murtha, A. D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409130 · Cited by 107
Improving Image Classification Using Semantic Attributes
Su, Y., & Jurie, F. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-012-0529-4 · Cited by 103
“Among the recent advances made on image classification, perhaps the most significant one is the representation of images by the statistics of local features, in particular through the introduction of histograms of textons (Leung and Malik 2001)”
Acharya, U. R., Bhat, S., Koh, J. E. W., et al. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.06.022 · Cited by 104
Seeland, M., Rzanny, M., Alaqraa, N., Wäldchen, J., & Mäder, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170629 · Cited by 106
“The encoding step is based on a codebook often termed “visual vocabulary” following the idea of document classification in text processing [57].”
MethodShape-based Invariant Texture Indexing
Xia, G.-S., Delon, J., & Gousseau, Y. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0312-3 · Cited by 100
Building models of animals from video
Ramanan, D., Forsyth, D. A., & Barnard, K. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.155 · Cited by 101
“Our results are worse than those reported for texture databases like CUReT [6], [43], [44].”
MethodResultData-driven image color theme enhancement
Wang, B., Yu, Y., Wong, T., et al. (2010) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1866158.1866172 · Cited by 102
“We cluster these texture descriptors using the K-means algorithms [Leung and Malik 2001](K=200).”
MethodRahim, T., Usman, M. A., & Shin, S. Y. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101767 · Cited by 101
“The Leung-Malik (LM) filter bank is used for extraction of the textural features, as LM is a multi resolution, multi-orientation, and multi-scale filter comprising 48 filters [99].”
MethodHuman Action Recognition Using Distribution of Oriented Rectangular Patches
İkizler-Cinbiş, N., & Duygulu, P. (2007) · Springer eBooks · doi:10.1007/978-3-540-75703-0_19 · Cited by 101
“The distance between frames is calculated using Chi-square distance between the histograms (as in [14]).”
MethodGrassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification
Wei, X., Zhang, Y., Gong, Y., Zhang, J., & Zheng, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_22 · Cited by 100
“The Bag-of-Words (BoW) framework [2, 3] used vector quantization for hard visual words assignment.”
MethodPatch detection for pavement assessment
Radopoulou, S. C., & Brilakis, I. (2015) · Automation in Construction · doi:10.1016/j.autcon.2015.03.010 · Cited by 101
“The selection of those filters is based on the following reasons: First, the Leung andMalik filterswere applied formaterial classification using texture information and the Schmid filters were used to create texture-based descriptors for content-based image retrieval.”
Abnormal image detection in endoscopy videos using a filter bank and local binary patterns
Nawarathna, R. D., Oh, J., Muthukudage, J., Tavanapong, W., Wong, J., de Groen, P. C., & Tang, S. J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.02.064 · Cited by 100
“We cluster the response vectors from each texture using k-means clustering [7,13] with the aim of finding a set of cluster centers This is because, in general a texton is a cluster center from the k-means clustering of response vectors.”
MethodDebats, S. R., Luo, D., Estes, L. D., Fuchs, T. J., & Caylor, K. K. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.03.010 · Cited by 101
Classifying materials in the real world
Barbara Caputo, B., Hayman, E., Fritz, M., & Eklundh, J. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.05.005 · Cited by 98
“Cula and Dana [4] adapted the method of Leung and Malik to form a faster, simpler and more accurate classifier.”
BackgroundMethodInfluentialTexture Analysis and Its Applications in Biomedical Imaging: A Survey
Ghalati, M. K., Nunes, A., Ferreira, H. R. L., Serranho, P., & Bernardes, R. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3115703 · Cited by 110
Zhou, M., Hall, L., Goldgof, D., Russo, R., Balagurunathan, Y., Gillies, R., & Gatenby, R. (2014) · Translational Oncology · doi:10.1593/tlo.13730 · Cited by 97
Binary Gabor pattern: An efficient and robust descriptor for texture classification
Zhang, L., Zhou, Z., & Li, H. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6466800 · Cited by 97
“INTRODUCTION Recent years have witnessed a growing interest in designing effective schemes for texture classification.”
BackgroundMMSS: Multi-modal Sharable and Specific Feature Learning for RGB-D Object Recognition
Wang, A., Cai, J., Lu, J., & Cham, T. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.134 · Cited by 97
“spin images [14] and SIFT descriptors [23] for depth images, while textons [21], color histograms [1] and SIFT descriptors were used for color images.”
MethodAutomatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning
Nguyen, T. H., Sridharan, S., Macias, V., Kajdacsy‐Balla, A., Melamed, J., Do, M. N., & Popescu, G. (2017) · Journal of Biomedical Optics · doi:10.1117/1.jbo.22.3.036015 · Cited by 98
“To capture the texture expression of the biopsies, we use the “texton” framework proposed by Julesz(37) and later expanded by Leung and Malik.(38,39) The framework has demonstrated great success in solving several computer vision problems, e.”
MethodInfluentialA Texton-Based Approach for the Classification of Lung Parenchyma in CT Images
Gangeh, M. J., Sørensen, L., Shaker, S. B., Kamel, M. S., de Bruijne, M., & Loog, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15711-0_74 · Cited by 97
“In this section, the principle of texton-based texture classification is reviewed [9, 11, 12].”
BackgroundCNN for saliency detection with low-level feature integration
Li, H., Jiang, C., Lu, H., & Chi, Z. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.11.056 · Cited by 97
Differential Angular Imaging for Material Recognition
Xue, J., Zhang, H., Dana, K., & Nishino, K. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.734 · Cited by 97
“Texture recognition, the classification of 3D texture images and bidirectional texture functions, traditionally relied on hand-designed 3D image features and multiple views [8, 24].”
BackgroundModeling and Classifying Breast Tissue Density in Mammograms
Bosch, A., Muñoz, X., Oliver, A., & Martı́, J. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.188 · Cited by 96
“Reviewing the literature of texture and materials classification we can also learn [13, 24].”
Background
Metadata
- Authors
- Leung, T., & Malik, J.
- Affiliations
- University of California, Berkeley (US)
- ORCID
- Malik, Jitendra
- Venue
- International Journal of Computer Vision
- Published
- 2001-06
- Volume
- 43
- Issue
- 1
- Pages
- 29-44
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1484228140
- Semantic Scholar
- 90d6e7f2202f754d8588f9536e3f5b4a24701f24
- Cited by
- 1,498
- References
- 45
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 18 · 2023: 17 · 2022: 33 · 2021: 30 · 2020: 48 · 2019: 78 · 2018: 74 · 2017: 94 · 2016: 115 · 2015: 116 · 2014: 122 · 2013: 91 · 2012: 81
- FWCI
- 27.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- material identification, natural textures, surface reflectance, surface normals, appearance modeling, Gaussian derivative filters, texture clustering, surface patches
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