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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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Items 301–400 of 1,001
Zhang, L., Yang, G., & Ye, X. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.2.024001 · Cited by 56
Classifier based graph construction for video segmentation
Khoreva, A., Galasso, F., Hein, M., & Schiele, B. (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.2015.7298697 · Cited by 55
“Texture information may be encoded (cf. [24] for more details) with (a subset of) the textons designed by Leung and Malik [31].”
MethodNatural textures classification in area V4 of the macaque monkey
Arcizet, F., Jouffrais, C., & Girard, P. (2008) · Experimental Brain Research · doi:10.1007/s00221-008-1406-9 · Cited by 55
Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning
Owens, A., Wu, J., McDermott, J. H., Freeman, W. T., & Torralba, A. B. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1083-5 · Cited by 61
“To study this, we replaced our sound texture features with (512-dimensional) visual texton histograms (Leung and Malik 2001), using the parameters from Xiao et al.”
Deep Multiple-Attribute-Perceived Network for Real-World Texture Recognition
Zhai, W., Cao, Y. H., Zhang, J., & Zha, Z.-J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00371 · Cited by 55
“Typical local features used in texture representation include various filter banks [17, 32], gray level co-occurrence matrix [12], LBP [24], SIFT [20] and HOG [9].”
MethodSlip Prediction Using Visual Information
Angelova, A., Matthies, L. H., Helmick, D. M., et al. (2006) · doi:10.15607/rss.2006.ii.014 · Cited by 55
“Schematic of the terrain classification algorithm [16], [24].”
Tracking Using Multilevel Quantizations
Hong, Z., Wang, C., Mei, X., Prokhorov, D. V., & Tao, D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_11 · Cited by 55
“The texture feature is generated by the Leung-Malik (LM) Filter Bank [36], which consists of the first and second derivatives of Gaussians at 6 orientations and 3 scales, 8 Laplacian of Gaussian (LOG) filters, and 4 Gaussian filters.”
MethodA Klein-Bottle-Based Dictionary for Texture Representation
Perea, J. A., & Carlsson, G. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0676-2 · Cited by 53
“…texture images which has proven to be highly effective in multi-class classification tasks, is the histogram of texton occurrences (Crosier and Griffin 2010; Hayman et al. 2004; Jurie and Triggs 2005; Lazebnik et al. 2005; Leung and Malik 2001; Varma and Zisserman 2005, 2009; Zhang et al. 2007).”
BackgroundMethodInfluentialZhang, H., Qiu, D., Wu, R., Deng, Y., Ji, D., & Li, T. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.03.017 · Cited by 55
Colour and Texture Descriptors for Visual Recognition: A Historical Overview
Bianconi, F., Fernández, A., Smeraldi, F., & Pascoletti, G. (2021) · Journal of Imaging · doi:10.3390/jimaging7110245 · Cited by 59
“In their original model they used a bank of N fil = 36 Gaussian derivative filters at three scales and six orientations [30], and later on extended this number to 48 with the addition of 12 rotationally invariant filters [31].”
BackgroundMethodLim, J. J., Arbeláez, P., Gu, C., et al. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459436 · Cited by 53
“Texture is encoded by following the texton approach [15], where filter-bank responses are clustered with the k-means algorithm.”
MethodSelf-citationUnsupervised Discovery of Mid-Level Discriminative PatchesPreprint
Singh, S., Gupta, A., & Efros, A. (2012) · arXiv (Cornell University) · doi:10.1184/r1/6561473 · Cited by 71
Co-reranking by mutual reinforcement for image search
Yao, T., Mei, T., & Ngo, C. (2010) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1816041.1816048 · Cited by 52
“We further use K-means to cluster the similar patches into “visual words,” and use Bag-of-Word (BoW) to represent each image as it has proven to be effective for object and scene retrieval [13, 14, 16, 22].”
MethodClassification of defects with ensemble methods in the automated visual inspection of sewer pipes
Wu, W., Liu, Z., & He, Y. L. (2013) · Pattern Analysis and Applications · doi:10.1007/s10044-013-0355-5 · Cited by 52
“bank and Leung-Malik (LM) filter bank, are used for comparison in this study [16, 17].”
BackgroundMethodHistogram-based search: A comparative study
Sizintsev, M., Derpanis, K. G., & Hogue, A. (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.2008.4587654 · Cited by 51
“Example applications include: object detection (e.g., [21, 9]), human detection (e.g., [8]), texture analysis (e.g., [13, 14]) and tracking (e.g., [2, 6, 5]).”
BackgroundUnderstanding Image Representations by Measuring Their Equivariance and Equivalence
Lenc, K., & Vedaldi, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1098-y · Cited by 98
“Notable examples include textons (Leung and Malik 2001), histogram of oriented gradients (SIFT Lowe 2004) and HOG Dalal and Triggs 2005), bag of visual words (Csurka et al.”
BackgroundCompact color–texture description for texture classification
Khan, F. S., Anwer, R. M., van de Weijer, J., et al. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.07.020 · Cited by 51
Shape classification using invariant features and contextual information in the bag-of-words model
Ramesh, B., Xiang, C., & Lee, T. H. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.09.019 · Cited by 51
“The descriptors obtained from the training images are collectively used to obtain a codebook, using VLFeat's [54] implementation of K-means with accelerated Elkan algorithm for optimization.”
MethodInfluentialRGBD Co-saliency Detection via Bagging-Based Clustering
Song, H., Liu, Z., Xie, Y., et al. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2615293 · Cited by 51
“The details of these features are as follows (along with the dimension of feature in the parenthesis): average color of each channel in the RGB, HSV, and L∗a∗b∗ color spaces (9); absolute responses (3) of Leung and Malik’s (LM) filters [23] on the color image; average depth value and depth”
BackgroundSynthesis and rendering of bidirectional texture functions on arbitrary surfaces
Liu, X., Hu, Y., Zhang, J., et al. (2004) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2004.1272727 · Cited by 51
Recognizing Materials from Virtual Examples
Li, W., & Fritz, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_25 · Cited by 49
“There is a long tradition of analyzing the visual appearance of texture and deriving good representations for this task [2, 3].”
BackgroundInteractive Image Segmentation Based on Level Sets of Probabilities
Liu, Y., & Yu, Y. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.77 · Cited by 50
“In the preprocessing stage, we perform various low-level image processing operations, including edge detection, edge field computation, Gabor filtering, and projected color histogram computation.”
MethodRotation–Covariant Texture Learning Using Steerable Riesz Wavelets
Depeursinge, A., Foncubierta–Rodríguez, A., Ville, D. V. D., et al. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2295755 · Cited by 50
“, gray–level co–occurrence matrices (GLCMs), run–length encoding (RLE), oriented filterbanks and wavelets (Gaussian, Gabor, Leung–Malik (LM), Maximum Response (MR) [6], [32], [42], [45], [60], non–separable wavelets), and histograms of oriented gradients (HOG) used in scale–invariant feature transform (SIFT) [34]) all…”
Boisvert, J. F. G., & Bruce, N. D. B. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.05.047 · Cited by 50
“The Leung–Malik (LM) filter bank The LM filter bank [34] consists of 4 Gaussians filters corre-”
MethodUniform and Variational Deep Learning for RGB-D Object Recognition and Person Re-Identification
Ren, L., Lu, J., Feng, J., et al. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2915655 · Cited by 50
“Existing RGB-D object recognition and person re-identification methods focus on designing hand-crafted feature descriptors for both RGB and depth images with some prior knowledge [22]–[27], or learning feature representations in a data-driven manner [1], [28], [29].”
BackgroundMethodBilayer Segmentation of Webcam Videos Using Tree-Based Classifiers
Yin, P., Criminisi, A., Winn, J. M., et al. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.65 · Cited by 49
Effective texture classification by texton encoding induced statistical features
Xie, J., Lei Zhang, A., You, J., et al. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.08.014 · Cited by 49
“Leung and Malik [11] proposed to build a vocabulary of micro-structures, i.”
BackgroundMethodGraph-Based Segmentation for RGB-D Data Using 3-D Geometry Enhanced Superpixels
Yang, J., Gan, Z., Li, K., et al. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2340032 · Cited by 49
“Leung and Malik [54] laid the framework of textons in the influential work.”
BackgroundMethodQuantifying collagen fiber orientation in breast cancer using quantitative phase imaging
Majeed, H., Okoro, C., Kajdacsy‐Balla, A., Toussaint, K. C., & Popescu, G. (2017) · Journal of Biomedical Optics · doi:10.1117/1.jbo.22.4.046004 · Cited by 53
“Specifically, for each core image, we first computed the response to the Leung–Malik (LM) filter bank comprising gradient filters at 50 different orientations.(57,58) Each filter in the bank computed the directional image gradient using the first derivative of a Gaussian, oriented at α 1⁄4 mπ 50 , where m 1⁄4 0; 1; 2;…”
MethodMajeed, H., Nguyen, T. H., Kandel, M. E., Kajdacsy‐Balla, A., & Popescu, G. (2018) · Scientific Reports · doi:10.1038/s41598-018-25261-7 · Cited by 50
Capture and Synthesis of 3D Surface Texture
Dong, J., & Chantler, M. J. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-4641-6 · Cited by 49
“Leung and Malik (2001) used the K -means algorithm to obtain a vocabulary of 3D textons from 20 CUReT textures.”
BackgroundMethodInfluentialLindeberg, T. (2008) · Wiley Encyclopedia of Computer Science and Engineering · doi:10.1002/9780470050118.ecse609 · Cited by 49
“For example, the task of texture classification can be expressed as a classification and/or clustering problem on the multi-scale N-jet over regions in the image [21].”
BackgroundDeep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity
Chen, Z., Li, F., Quan, Y., Xu, Y., & Ji, H. (2021) · 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/cvpr46437.2021.00519 · Cited by 50
“[6, 11, 20, 32, 48, 49]) and BoVW of texture ( e.g .”
BackgroundBehera, T., Bakshi, S., Nappi, M., & Sa, P. K. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3239119 · Cited by 42
“Conventional classifiers employed machine learning techniques that extract the local features from the input pixel intensities through simple arithmetic combinations [17], [18].”
MethodTrainable Convolution Filters and Their Application to Face Recognition
Kumar, R., Banerjee, A., Vemuri, B. C., & Pfister, H. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.225 · Cited by 50
“Examples of convolution features include Haar features [38], Gabor features [31], Leung-Malik (LM) features [30] among others.”
BackgroundClassification of interstitial lung disease patterns using local DCT features and random forest
Anthimopoulos, M., Christodoulidis, S., Christe, A., & Mougiakakou, S. G. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945006 · Cited by 48
“Filter Bank Number of filters Favg (%) Leung-Malik [20] 48 76.”
BackgroundLearning Image Similarity from Flickr Groups Using Fast Kernel Machines
Wang, G., Hoiem, D., & Forsyth, D. A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.29 · Cited by 48
“Traditional approaches usually extract low-level visual features (e.g., texture features [20], [36], [46], shape features [4], [16], and gradient features [25], [9]) from images, and match them using distance metrics such as the euclidean distance, the Chi-Square distance, the histogram…”
BackgroundNovel texture-based descriptors for tool wear condition monitoring
Antić, A., Popovic, B. Z., Krstanović, L., et al. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.04.030 · Cited by 48
Zhang, L., Dolwani, S., & Ye, X. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_62 · Cited by 49
Bidirectional Imaging and Modeling of Skin Texture
Cula, O. G., Dana, K., Murphy, F., & Rao, B. K. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.836520 · Cited by 48
“Based on the classical work of [22], and also on recent algorithms [25], we follow the theory that there is a finite set of local structural features that can be found within a large collection of texture images from various samples.”
BackgroundMethodIllumination direction from texture shading
Koenderink, J. J., van Doorn, A. J., Kappers, A. M. L., et al. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.000987 · Cited by 48
The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISMPreprint
Allys, E., Levrier, F., Zhang, S., et al. (2019) · HAL · Cited by 48
More about VLAD: A leap from Euclidean to Riemannian manifolds
Faraki, M., Harandi, M. T., & Porikli, F. (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.2015.7299129 · Cited by 46
“Since the late nineties, Bag of Words (BoW) [40, 18, 30, 42] and its extensions [29, 19] have been the de facto driving force for image and video representation.”
BackgroundRGBD object recognition and visual texture classification for indoor semantic mapping
Filliat, D., Battesti, E., Bazeille, S., Duceux, G., Gepperth, A., Harrath, L., Jebari, I., Pereira, R., Tapus, A., Meyer, C., et al. (2012) · IEEE International Conference on Technologies for Practical Robot Applications · doi:10.1109/tepra.2012.6215666 · Cited by 48
“These filters encode the local spatial variation that characterize a texture (e.g., [18], [19]) and are used as pre-processing for a classification algorithm such as Support Vector Machine of Nearest Neighbour.”
MethodHandling label noise in video classification via multiple instance learning
Leung, T., Song, Y., & Zhang, Y. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126479 · Cited by 46
“The following features are also computed for each frame (with down-sampling): Hue-Saturation color histogram, texton histogram [18] with vocabulary size 1000, edge features including fraction of edge pixels and edge direction histogram, and face features [27] with the number of faces and the ratio of largest face area…”
MethodSelf-citationVisually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer
Niazi, M. K. K., Yao, K., Zynger, D. L., Clinton, S. K., Chen, J., Koyutürk, M., LaFramboise, T., & Gürcan, M. N. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2565515 · Cited by 46
“At the first stage, one may choose from multiple filter banks [19], [21], [22].”
BackgroundX-Ray Scattering Image Classification Using Deep Learning
Wang, B., Yager, K. G., Yu, D., & Hoai, M. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.83 · Cited by 47
“We compare this result with a shallow-learning method that is based on the Bag-of-Word approach [12, 14].”
ResultQuantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation
Bernardis, E., & Castelo‐Soccio, L. A. (2017) · Journal of Investigative Dermatology Symposium Proceedings · doi:10.1016/j.jisp.2017.10.010 · Cited by 46
Relief texture from specularities
Wang, J., & Dana, K. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.63 · Cited by 46
“Furthermore, the method can be extended to objects with arbitrary known BRDF.”
MethodDetermining Patch Saliency Using Low-Level Context
Parikh, D., Zitnick, C. L., & Chen, T. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_33 · Cited by 46
“The strategies of dense sampling [13, 16, 25] or random sampling [12] have shown to provide comparable or even better performance than interest points [7].”
BackgroundReflectance hashing for material recognition
Zhang, H., Dana, K., & Nishino, K. (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.2015.7298926 · Cited by 45
“We employ the same filter banks as in [19], comprised of Gaussian low-pass filters at four scales, Laplacian filters at four scales and eight gradient filters at different orientations and scales for a total of 24 filters (ranging in size from 7 × 7 to 25 × 25).”
Methodde Luis-Garcı́a, R., Rachid Deriche, B., & Alberola‐López, C. (2007) · Signal Processing · doi:10.1016/j.sigpro.2007.09.019 · Cited by 45
Texel-based texture segmentation
Todorović, S., & Ahuja, N. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459308 · Cited by 45
“Some methods allow random placement of texels, based on random tessellations [24], or based on representing texels as points and then tracking similar points within heuristic pixel neighborho ods [17].”
MethodProbabilistic Motion Diffusion of Labeling Priors for Coherent Video Segmentation
Wang, T., & Collomosse, J. P. (2011) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2177078 · Cited by 45
“To this end, we adopt textons [33] which have been proven effective in categorizing materials [34] and generic object classes [35], [8], [36].”
MethodShapeme histogram projection and matching for partial object recognition
Shan, Y., Sawhney, H., Matei, B., et al. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.83 · Cited by 45
“Shapeme histogram is used by [9] and [10] for texture recognition.”
MethodImproving Saliency Detection Via Multiple Kernel Boosting and Adaptive Fusion
Zhou, X., Liu, Z., Sun, G., et al. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2536743 · Cited by 45
“For training the regressor, we select the training set {xi, yi}nsi=1, where ns is the number of samples based on the initial saliency map MI , which can be generated using any saliency model for images.”
MethodInteractive Image Segmentation Using Constrained Dominant Sets
Zemene, E., & Pelillo, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_17 · Cited by 45
“As mentioned above, the vertices of our graph represents over-segments and edge weights (similarities) are built from the median of the color of all pixels in RGB, HSV, and L*a*b* color spaces, and Leung-Malik (LM) Filter Bank [25].”
MethodIrradiation direction from texture
Koenderink, J. J., & Pont, S. C. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001875 · Cited by 45
“However, due to photometric interactions such as body and cast shadow, shading, vignetting and multiple scattering at the mesoscale (the scale of the corrugations), the local irradiance will vary and consequently an image texture will be generated.(2,16) The resulting texture will critically depend upon the irradiatio…”
Deep convolutional filter banks for texture recognition and segmentationPreprint
Cimpoi, M., Maji, S., & Vedaldi, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.6836 · Cited by 49
“In the simplest terms a texture is characterized by the arrangement of local patterns, as captured in early works [25, 38] by the distribution of local “filter bank” responses.”
BackgroundGround Truth Data, Content, Metrics, and Analysis
Krig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_7 · Cited by 48
Automatic joint classification and segmentation of whole cell 3D images
Narasimha, R., Ouyang, H. J., Gray, A., et al. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.08.009 · Cited by 44
The Synthesizability of Texture Examples
Dai, D., Riemenschneider, H., & Gool, L. V. (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.2014.387 · Cited by 46
“Using image filters has become one of the most popular tools for texture analysis [20, 28] and synthesis [40].”
BackgroundMethodEnhanced biologically inspired model
Huang, Y., Huang, K., Wang, L., Tao, D., Tan, T., & Li, X. (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.2008.4587599 · Cited by 44
“Moreover, though bag-offeatures [20] and bag-of-keypoints [11] are efficient, they abandon structure information.”
BackgroundEvaluation of local features for scene classification using VHR satellite images
Chen, L., Yang, W. T., Xu, K., & Xu, T. (2011) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse.2011.5764800 · Cited by 44
“The Leung-Malik (LM) set is a multi-scale, multi-orientation bank of 48 filters [13].”
Hair detection, segmentation, and hairstyle classification in the wild
Muhammad, U. R., Svanera, M., Leonardi, R., & Benini, S. (2018) · Image and Vision Computing · doi:10.1016/j.imavis.2018.02.001 · Cited by 45
“Later on Malik along with Leung [45] studies the recognition of surfaces made from different materials such as concrete, rug, marble, or leather on the basis of their textural appearance.”
BackgroundA Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images
Khan, A. M., Sirinukunwattana, K., & Rajpoot, N. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2447008 · Cited by 43
Weimer, D., Thamer, H., & Scholz‐Reiter, B. (2013) · Procedia CIRP · doi:10.1016/j.procir.2013.05.059 · Cited by 43
Image filtering techniques for medical image post-processing: an overview
Behrenbruch, C. P., Petroudi, S., Bond, S., Declerck, J. D., Leong, F. J. W.-M., & Brady, J. M. (2004) · British Journal of Radiology · doi:10.1259/bjr/17464219 · Cited by 43
Texture classification via patch-based sparse texton learning
Xie, J., Lei Zhang, A., You, J., & Zhang, D. D. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5651387 · Cited by 43
“Leung and Malik [6] proposed to classify texture images by using three dimensional (3D) textons, which are cluster centers of filter responses over a stack of images with representative viewpoints and illuminations.”
BackgroundInfluentialLocal binary circumferential and radial derivative pattern for texture classification
Wang, K., Bichot, C., Li, Y., & Li, B. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.01.034 · Cited by 43
Majeed, H., Keikhosravi, A., Kandel, M. E., Nguyen, T. H., Liu, Y., Kajdacsy‐Balla, A., Tangella, K. V., Eliceiri, K. W., & Popescu, G. (2019) · Scientific Reports · doi:10.1038/s41598-019-50143-x · Cited by 42
Ilp: a short look back and a longer look forward
Page, D., & Srinivasan, A. (2003) · Cited by 43
Rotation Invariant Local Frequency Descriptors for Texture Classification
Maani, R., Kalra, S., & Yang, Y. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2249081 · Cited by 42
“Leung and Malik [10], Cula and Dana [11], and Varma and Zisserman [12] employ spatial filters on the textures, and used the histogram of filter response cluster centers as features.”
MethodTaxonomic classification for web-based videos
Song, Y., Zhao, M., Yagnik, J., & Xiaoyun, W. (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.2010.5540124 · Cited by 43
“Texton histogram is computed as in [9].”
Multi-fractal detrended texture feature for brain tumor classification
Reza, S. M. S., Mays, R., & Iftekharuddin, K. M. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083596 · Cited by 42
“[8] extract shape and Gabor-like texton [9] features from perfusion and conventional MRIs for tumor grading.”
BackgroundMalof, J. M., Bradbury, K., Collins, L. M., Newell, R. G., Serrano, A., Wu, H., & Keene, S. M. (2016) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera.2016.7884446 · Cited by 42
Glioblastoma and Survival Prediction
Shboul, Z. A., Vidyaratne, L., Alam, M., & Iftekharuddin, K. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75238-9_31 · Cited by 42
“The texture representations are piecewise triangular prism surface area (PTPSA) [16], multifractional Brownian motion (mBm) [17], Generalized multifrational Brownian motion (GmBm) [18] [19], and five representations of Texton filters [20], respectively.”
BackgroundCombining powerful local and global statistics for texture description
Xu, Y., Huang, S., Ji, H., et al. (2009) · 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.2009.5206741 · Cited by 42
“[3], [6], [7], [8], [10], [11], [12], [19], [20]).”
Semi-supervised learning and feature evaluation for RGB-D object recognition
Cheng, Y., Zhao, X., Huang, K., & Tan, T. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.05.007 · Cited by 41
Discriminative tag learning on YouTube videos with latent sub-tags
Yang, W., & Toderici, G. D. (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.2011.5995402 · Cited by 41
“In addition, we also compute a Hue-Saturation color histogram [13] and a Texton histogram [17] for every frame.”
MethodRecognizing jumbled images: The role of local and global information in image classification
Parikh, D. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126283 · Cited by 41
“For texture, the block is filtered with a bank of 12 filters at 6 orientations and 2 scales [28].”
MethodTompkin, J., Kim, K. I., Kautz, J., et al. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185564 · Cited by 41
“We use bag-of-visual-word -type histograms of SIFT features [Csurka et al. 2004; Leung and Malik 2001] with a standard set of parameters (#pyramid levels = 3, codebook size = 200).”
MethodResultSingle-Histogram Class Models for Image Segmentation
Schroff, F., Criminisi, A., & Zisserman, A. (2006) · Lecture notes in computer science · doi:10.1007/11949619_8 · Cited by 41
“During training a vocabulary of V visual words (also called textons, [13, 20, 21, 22]) is built by clustering the feature vectors extracted from many training images (step S2).”
BackgroundZhang, H., Zha, Z.-J., Yan, S., et al. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2393365 · Cited by 40
“Texture descriptors were computed for each pixel as the 48-dimensional responses of texton filter banks [14].”
MethodDerpanis, K. G. (2008) · Cited by 41
Tan, C.-W., & Kumar, A. (2012) · PolyU Institutional Research Archive (Hong Kong Polytechnic University) · Cited by 40
Knock-Knock: Acoustic object recognition by using stacked denoising autoencoders
Luo, S., Zhu, L., Althoefer, K. A., & Liu, H. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.03.014 · Cited by 40
A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion
Zhang, Q., Li, Y., Zhao, G., Man, P., Lin, Y., & Wang, M. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8860011 · Cited by 42
“In past decades, many effective filter banks, such as Leung–Malik [28], Schmid [15], and MR filters [8], have been used to generate textons.”
MethodProperties and applications of shape recipes
Torralba, A. B., & Freeman, W. T. (2003) · 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.2003.1211494 · Cited by 39
“Our learning-based approach relates to Leung and Malik’s learning 3-d textons [6], and builds on Pentland’s linear shading work [8].”
MethodRadon Representation-Based Feature Descriptor for Texture Classification
Liu, G., Lin, Z., & Yu, Y. T. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2013072 · Cited by 39
“…co-occurrence matrices [1], Gabor transforms [2], [3], bidirectional texture function [4], [5], local binary patterns [6]–[8], random fields [9], [10], autoregressive models [11], wavelet-based features [12], [13], textons [14]–[17], affine adaption [18]–[20], fractal dimension [21]–[24], SIFT…”
BackgroundRobust single-view instance recognition
Held, D., Thrun, S., & Savarese, S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487365 · Cited by 40
Text recognition in scene image and video frame using Color Channel selection
Bhunia, A. K., Kumar, G., Roy, P. P., et al. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4750-6 · Cited by 39
“Though, there exist some texture based descriptors [22-24, 54, 56-59], in this paper we have used four different texture features namely wavelet, Gabor, LBP and LPQ for color channel selection along with the feature described in [17[.”
MethodMulti-fractal texture features for brain tumor and edema segmentation
Reza, S., & Iftekharuddin, K. M. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2044264 · Cited by 39
“ Feature type 2(Spatial/texture feature): We employ our novel texture features such as fractal PTPSA, mBm [12] [13] [17] and regular Gabor-like texton [6] [12] to characterize the tumor surface variation which is expected to be different from the non-tumor region.”
BackgroundMethodBasic gray level aura matrices: theory and its application to texture synthesis
Qin, X., & Yang, Y.-H. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.43 · Cited by 41
“Recently, texton approaches have been extensively studied and successfully used in texture modeling by a number of researchers [ 14 , 29, 33].”
BackgroundGaussianMask: Uncertainty-aware Instance Segmentation based on Gaussian Modeling
Lee, S. I., & Kim, H. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956515 · Cited by 38
“mask using position-sensitive maps [29], [30], or combining semantic segmentation logits and direction prediction logits [31].”
MethodSoldado, R. A. M., & Almagro, C. U. (2012) · Cited by 38
Remote Sensing Image Classification: No Features, No Clustering
Cui, S., Schwarz, G., & Datcu, M. P. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2495267 · Cited by 38
Mittal, H., & Saraswat, M. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1595-4_18 · Cited by 38
Deep Learning Approaches to Image Texture Analysis in Material Processing
Liu, X., & Aldrich, C. (2022) · Metals · doi:10.3390/met12020355 · Cited by 38
Yang, L., Chen, W., Meer, P., Salaru, G., Goodell, L. A., Berstis, V., & Foran, D. J. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2020159 · Cited by 38
“texture-related research, including classification [19]–[21], segmentation [22], and synthesis [23].”
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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