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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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(1967) · Cited by 23,719
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927
Vector Quantization and Signal Compression
Gersho, A., & Gray, R. M. (1992) · Springer eBooks · doi:10.1007/978-1-4615-3626-0 · Cited by 7,055
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,500
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Multirate Systems and Filter Banks
Vaidyanathan, P. P. (1992) · CaltechAUTHORS (California Institute of Technology) · Cited by 5,457
Research works printed but not linked: 2
- Dana, K. and Nayar, S. 1999a. 3D textured surface modelling. In Proceedings Workshop on the Integration of Appearance and Geometric Methods in Object Recognition, pp. 46-56.
- 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 501–600 of 1,001
On Exploring Multiplicity of Primitives and Attributes for Texture Recognition in the Wild
Zhai, W., Cao, Y. H., Zhang, J., Xie, H., Tao, D., & Zha, Z.-J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3325230 · Cited by 24
“The classic approaches, such as texton histograms [6], [7] and bag-of-words [8], [9] usually apply a set of hand-crafted filter banks to transform texture image into local features and then aggregate them into a global representation.”
MethodEfficient coding of natural images in the mouse visual cortex
Bolaños, F., Orlandi, J. G., Aoki, R., Jagadeesh, A. V., Gardner, J. L., & Benucci, A. (2024) · Nature Communications · doi:10.1038/s41467-024-45919-3 · Cited by 24
“This has been explored via diverse computational approaches: in the field of computer graphics [12], via entropy-based methods [13–15], using wavelet approaches [16, 17], and, more recently, in machine learning implementations based on deep convolutional neural networks [18–21].”
Testing new variants of the Beckmann–Kirchhoff model against radiance data
Ragheb, H., & Hancock, E. R. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.11.004 · Cited by 24
“We use the BRDF measurements from the CUReT database to compare the predictions of the Fresnel correction process with a number of alternative models.”
MethodExploiting publicly available cartographic resources for aerial image analysis
Seo, Y., Urmson, C., & Wettergreen, D. S. (2012) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2424321.2424336 · Cited by 24
“To produce a texton dictionary, we applied Leung and Malik’s filter bank [14](2) to 23 highway ortho-images collected separately from test images, to obtain the initial filter responses and then clustered these filter responses into the predefined number of groups (i.”
MethodYamazaki, K., & Inaba, M. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696739 · Cited by 24
“This approach is similar to a filter bank [6] [19], the most frequent application of which is texture classification [12].”
MethodTexture classification with minimal training images
Targhi, A. T., Geusebroek, J., & Zisserman, A. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761388 · Cited by 24
“Material recognition as a 3D texton modeling problem was introduced by Leung and Malik [18], triggered by the availability of the CURET collection [8].”
BackgroundBreast histopathology image segmentation using spatio‐colour‐texture based graph partition method
Belsare, A. D., Mushrif, M. M., Pangarkar, M. A., & Meshram, N. H. (2015) · Journal of Microscopy · doi:10.1111/jmi.12361 · Cited by 24
“Textons were first introduced by Julesz in 1981 as a unit of human texture perception (Leung & Malik, 2001).”
BackgroundKeen, S. C., Shiu, Y., Wrege, P. H., & Rowland, E. D. (2017) · The Journal of the Acoustical Society of America · doi:10.1121/1.4979476 · Cited by 27
Illumination-invariant texture classification using simple training images
Drbohlav, O., & Chantler, M. J. (2005) · Cited by 24
High-Order Statistics of Microtexton for HEp-2 Staining Pattern Classification
Han, X., Wang, J., Xu, G., et al. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2320294 · Cited by 23
“filter bank (a number of filters or wavelets) responses [24], [33], [37].”
BackgroundMaani, R., Kalra, S., & Yang, Y. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2351620 · Cited by 23
“…matrix (GLCM) [6], the gray level Aura matrix (GLAM) [7], the Run Length Matrix (RLM) [8], filter responses in the frequency [9] and spatial [10], [11] domains, Wavelets [12], Orientation Pyramid [13], Markov Random Fields (MRFs) [14], Gaussian MRFs [15], spin images [16], and the Local…”
BackgroundKrig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_2 · Cited by 26
A multilevel pooling scheme in convolutional neural networks for texture image recognition
Lyra, L. O., Fabris, A. E., & Florindo, J. B. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111282 · Cited by 23
LBP and Color Descriptors for Image Classification
Banerji, S., Verma, A., & Liu, C. (2012) · Intelligent systems reference library · doi:10.1007/978-3-642-28457-1_10 · Cited by 23
“Whereas, the orderless bag-of-words methods [12, 21, 18] are simpler and computationally efficient, though they are not able to represent the geometric structure of the object or to distinguish between foreground and background features.”
MethodKenny, A., & Katz, A. (2011) · Materials and Structures · doi:10.1617/s11527-011-9786-x · Cited by 23
Task-Specific Image Partitioning
Kim, S., Nowozin, S., Kohli, P., et al. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2218822 · Cited by 23
“The proposed correlation clustering merges superpixels into disjoint regions of homogeneity over a superpixel graph.”
MethodTexture sparseness for pixel classification of business document images
Cote, M., & Albu, A. B. (2014) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-014-0217-8 · Cited by 25
“The representation of texture through pixel-based responses to a filter bank is highly versatile, but can present redundancies [41].”
BackgroundMethodInfluentialIncremental indexing and distributed image search using shared randomized vocabularies
Marée, R., Denis, P., Wehenkel, L. A., & Geurts, P. (2010) · Multimedia Information Retrieval · doi:10.1145/1743384.1743405 · Cited by 24
“In image classification and retrieval, bag-of-features approaches [4, 16, 28] have recently become popular due to their conceptual simplicity and robust recognition performances [38].”
BackgroundLiu, C., Yang, G., & Gu, J. (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.2013.188 · Cited by 23
Semi-supervised manifold-embedded hashing with joint feature representation and classifier learning
Song, T., Cai, J., Zhang, T., Gao, C., Meng, F., & Wu, Q. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.03.004 · Cited by 23
Chen, J., Liu, H., Hou, J., Yang, M., & Deng, M. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7060213 · Cited by 23
“The color features represent the mean of RGB, Lab, and HSV, and the texture features are calculated by using the local binary pattern (LBP) [25] and the Lee–Malik (LM) filter [26].”
MethodAcharya, U. R., Meiburger, K. M., Koh, J. E. W., Ciaccio, E. J., Arunkumar, N., See, M., Taib, N. A. M., Vijayananthan, A., Rahmat, K., Fadzli, F., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2898121 · Cited by 23
“tives for six different directions and three scales, four Gaussian filters, and eight Laplacian of Gaussian filters [28].”
BackgroundMethodDistributed Constrained Online Learning
Paternain, S., Lee, S., Zavlanos, M. M., et al. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2999671 · Cited by 23
“The feature extraction is done as in [5], a procedure inspired in the two-dimensional texton [37].”
MethodGlioma grading using structural magnetic resonance imaging and molecular data
Reza, S. M. S., Samad, M. D., Shboul, Z. A., et al. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.2.024501 · Cited by 23
Bozkurt, A., Köse, K., Coll‐Font, J., Alessi‐Fox, C., Brooks, D. H., Dy, J., & Rajadhyaksha, M. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-90328-x · Cited by 28
Xiang, Z., You, Z., Qian, M., Zhang, J., & Hu, X. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0321-7 · Cited by 24
MonoStream: A Minimal-Hardware High Accuracy Device-free WLAN Localization SystemPreprint
Sabek, I., & Youssef, M. A. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.0768 · Cited by 23
“To capture small variations in the CSI profiles between adjacent locations, MonStream borrows techniques from the object recognition domain [10, 18], where the CSI profile of a particular virtual link at a certain location is treated as an image.”
MethodCorrelated and Individual Multi-Modal Deep Learning for RGB-D Object RecognitionPreprint
Wang, Z., Lu, J., Lin, R., Feng, J., & Zhou, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.01655 · Cited by 34
“For example, Lai et al. [25] exploited a bunch of hand-crafted features including color histograms [1], textons [29] and SIFT [30] for the RGB channel, spin images [22] and SIFT [30], and multiple shape features for the depth channel for feature representation.”
MethodLearning Optimal Representations with the Decodable Information BottleneckPreprint
Dubois, Y., Kiela, D., Schwab, D. J., & Vedantam, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12789 · Cited by 26
“The right plot (labeled DIB Same) is a baseline that always has four Monte Carlo samples, but uses [1, 3, 5] predictors with different decoders with different initializations each predicting the same N (for a total of [4, 12, 20] predictors as in the left plots).”
BackgroundExemplar-Based Colour Constancy
Joze, H. R. V., & Drew, M. S. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.26 · Cited by 22
“The histogram of frequency of textons belonging to this dictionary is a common description for texture detection [19] although other local descriptors such as SIFT [21] may used instead of the MR8 filter bank.”
MethodGangeh, M. J., Naini, A. S., Kamel, M. S., & Czarnota, G. J. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556788 · Cited by 22
“Leung and Malik were the first who could propose a complete texture classification system based on textons [15].”
BackgroundMethodD4L: Decentralized Dynamic Discriminative Dictionary Learning
Koppel, A., Warnell, G., Stump, E., & Ribeiro, A. (2017) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2017.2662618 · Cited by 22
“For each patch, we compute the following features: the mean, variance, skewness and kurtosis of the color values in each of the red, green, and blue, and Lab color channels of the onboard camera; the texton histogram of the image, obtained using the method described in [42].”
MethodMulti-scale region composition of hierarchical image segmentation
Peng, B., Al‐Huda, Z., Xie, Z., et al. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09346-y · Cited by 22
DFAEN: Double-order knowledge fusion and attentional encoding network for texture recognition
Yang, Z., Lai, S., Hong, X., Shi, Y., Cheng, Y., & Qing, C. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118223 · Cited by 23
Periocular biometrics in mobile environment
Pereira, T. D. F., & Marcel, S. (2015) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2015.7358785 · Cited by 22
“[15] encoded the periocular with several features (LBP histograms, HoG, SIFT , GIST [12] and LMF (Leung-Malik Filters) [7]).”
BackgroundReliability-Driven, Spatially-Adaptive Regularization for Deformable Registration
Tang, L. Y. W., Hamarneh, G., & Rafeef Abugharbieh, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14366-3_16 · Cited by 22
Li, Y., Tan, Y., Li, Y., et al. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2387850 · Cited by 22
“In this letter, multiple features of the image block are composed of the spectral-based histogram where the primitive is the spectral feature vector, the sift-based histogram where the primitive is the sift descriptor [15], and the texture-based histogram where the primitive is the MR8 filter bank response vector [16].”
Statistical Acquisition of Texture Appearance
Ngan, A., & Durand, F. (2006) · doi:10.2312/egwr/egsr06/031-040 · Cited by 22
Zhang, H., Dana, K., & Nishino, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_49 · Cited by 25
“Traditional methods of texton histograms [9, 10, 25, 40] have a weakness in their discriminatory power.”
MethodFeature Learning and Deep Learning Architecture Survey
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_10 · Cited by 22
Domain Adaptation for Robust Classification of Disparate Hyperspectral Images
Zhou, X. H., & Prasad, S. (2017) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2017.2752150 · Cited by 22
“as a complex wavelet, for example a Morlet wavelet, due to better frequency and directional resolution [37].”
BackgroundClassification of scaled texture patterns with transfer learning
Anaam, A., & Rushdi, M. A. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.033 · Cited by 22
Susan, S., & Devi, K. M. R. (2019) · Pattern Analysis and Applications · doi:10.1007/s10044-019-00811-5 · Cited by 22
“The feature set in [31] is proved to outperform the rotation-invariant LBP [35], LBPV [36] and the scale-invariant MRS4 filter bank features [37] for texture identification and classification [31].”
Image classification using SLIC superpixel and FAAGKFCM image segmentation
Singh, N. K., Singh, N. J., & Kumar, W. K. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2019.0255 · Cited by 22
Texture Based Mammogram Classification and Segmentation
Gong, Y. C., Brady, M., & Petroudi, S. (2006) · Lecture notes in computer science · doi:10.1007/11783237_83 · Cited by 22
Dictionary Learning in Texture Classification
Gangeh, M. J., Ghodsi, A., & Kamel, M. S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21593-3_34 · Cited by 22
“As mentioned in the introduction, dictionary learning was introduced to the field of texture analysis by Julesz theory that stated textures can be represented using a few primitive elements [5] and following the work done in [7, 8, 9, 10, 11] that initiated the texton-based approach in texture classification.”
BackgroundMethodCreating Efficient Visual Codebook Ensembles for Object Categorization
Luo, H., Wei, H., & Lai, L. L. (2010) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2010.2064300 · Cited by 22
“In a previous study [16], the k-means was employed to acquire a small vocabulary from a large image collection of different materials.”
MethodDeep Learning for Single-View Instance RecognitionPreprint
Held, D., Thrun, S., & Savarese, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.08286 · Cited by 26
“We compare to the method of [28], which combines a number of visual features, including dense SIFT [38], texton histograms [36], and a color histogram.”
MethodRecognizing locations with Google Glass: A case study
Altwaijry, H., Moghimi, M. K., & Belongie, S. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836105 · Cited by 21
“Features included Color Histograms, Texton Histograms [25] and GIST [32].”
MethodImage Tagging with Social Assistance
Yang, Y., Gao, Y., Zhang, H., et al. (2014) · International Conference on Multimedia Retrieval · doi:10.1145/2578726.2578731 · Cited by 21
“Weextracted four types of visual features for image representation, including a 9D edge feature, a 512D color feature, a 512D texture feature [9] and a 4 , 096D bag-of-visual-word feature based on Scale Invariant Feature Transform (SIFT) descriptors [13].”
BackgroundTexture Analysis with Shape Co-occurrence Patterns
Liu, G., Xia, G.-S., Yang, W. T., & Zhang, L. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.288 · Cited by 21
“sequently utilized to describe the statistical arrangement of texture [1,2,9].”
MethodAtlas-based imaging data analysis tool for quantitative mouse brain histology (AIDAhisto)
Pallast, N., Wieters, F., Fink, G. R., & Aswendt, M. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108394 · Cited by 20
Multi-Scale Boosting Feature Encoding Network for Texture Recognition
Song, K., Yang, H. S., & Yin, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3051003 · Cited by 21
Melanoma and Other Skin Lesion Detection Using Smart Handheld Devices
Zouridakis, G., Wadhawan, T., Situ, N., Hu, R., Yuan, X., Lancaster, K., & Queen, C. M. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-2172-0_30 · Cited by 21
“To address the problem of imbalanced classes in a small sample size classification, several technique have been developed based on resampling, namely, undersampling the majority class [100, 105], oversampling the minority class [100–107], or both [108]; one-class learning methods built to recognize samples from a give…”
MethodStereo vision with texture learning for fault-tolerant automatic baling
Blas, M. R., & Blanke, M. (2010) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2010.10.012 · Cited by 21
Multi-structure local binary patterns for texture classification
He, Y., Sang, N., & Gao, C. (2012) · Pattern Analysis and Applications · doi:10.1007/s10044-011-0264-4 · Cited by 21
“Leung and Malik [8] create 3D textons from a stack of texture images under different captured conditions for the texture classification.”
BackgroundMethodRobust Illumination Invariant Texture Classification Using Gradient Local Binary Patterns
He, Y., & Sang, N. (2011) · International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM) · doi:10.1109/m2rsm.2011.5697422 · Cited by 21
“Following the filtering ideas, Leung and Malik[3] use filters to create 3D textons for texture classification.”
MethodA Deep Learning Paradigm for Detection of Harmful Algal Blooms
Kumar, A., & Bhandarkar, S. M. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.88 · Cited by 21
“The computer vision research community has invested significant research effort in texture recognition and classification [5, 17, 33].”
BackgroundFrom Image to Text Classification: A Novel Approach based on Clustering Word Embeddings
Butnaru, A. M., & Ionescu, R. T. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.211 · Cited by 22
“There are plenty of research studies [23, 24, 26, 30, 36] in this direction.”
BackgroundMethodInfluentialEpelde, I., Líria, P., de Santiago, I., et al. (2021) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2021.105588 · Cited by 21
Global pattern analysis and classification of dermoscopic images using textons
Sadeghi, M., Lee, T. K., McLean, D. I., et al. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911818 · Cited by 21
Multiple kernel learning for classification of diffuse lung disease using HRCT lung images
Vo, K. T., & Sowmya, A. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626113 · Cited by 21
Liu, X., & Aldrich, C. (2024) · Minerals Engineering · doi:10.1016/j.mineng.2024.108790 · Cited by 20
A data driven method for feature transformation
Dikmen, M., Hoiem, D., & Huang, T. S. (2012) · 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.2012.6248069 · Cited by 20
“A successful application of representing local appearances through clustering of filter responses has been studied by Leung and Malik [18] in the context of texture recognition, which further suggests that our approach may yield good results under the detection setting.”
MethodWang, L., Pedersen, P. C., Agu, E., et al. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.2.024002 · Cited by 20
“17,23,28 The standard texton generation process usually consists of two steps: (1) filtering and (2) clustering (more details about the texton generation can be found in Ref.”
BackgroundAnomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
Sohn, K., Yoon, J., Li, C., Lee, C., & Pfister, T. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00544 · Cited by 22
“Some applications relevant to our work include texture and material classification [35, 61, 33] and description [19, 11].”
BackgroundMethodUsing scalable computer vision to automate high-throughput semiconductor characterization
Siemenn, A. E., Aissi, E., Sheng, F., Tiihonen, A., Kavak, H., Das, B., & Buonassisi, T. (2024) · Nature Communications · doi:10.1038/s41467-024-48768-2 · Cited by 22
Introducing Space and Time in Local Feature-Based Endomicroscopic Image Retrieval
André, B., Vercauteren, T. K., Aymeric Perchant, B., Buchner, A. M., Wallace, M. B., & Ayache, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-11769-5_2 · Cited by 20
“2 and compared with the following methods taken as references: the standard sparse scale invariant SIFT method, the statistical approach of Haralick features [7, 8] and the texture retrieval method of Textons [9].”
MethodJoint Image and Word Sense Discrimination for Image Retrieval
Lucchi, A., & Weston, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_10 · Cited by 19
“Various spatial [26] and multiscale color and texton histograms [27] are combined for a total of”
BackgroundUnifying Color and Texture Transfer for Predictive Appearance Manipulation
Okura, F., Vanhoey, K., Bousseau, A., Efros, A. A., & George Drettakis, B. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12678 · Cited by 20
Gensure, R. H., Foran, D. J., Lee, V. M., et al. (2012) · Academic Radiology · doi:10.1016/j.acra.2012.04.015 · Cited by 20
“In this application, the LM filter bank (20) was used as the texton filter set.”
Susan, S., & Hanmandlu, M. (2013) · IET Image Processing · doi:10.1049/iet-ipr.2012.0527 · Cited by 20
A high-throughput framework to detect synapses in electron microscopy images
Navlakha, S., Suhan, J. P., Barth, A. L., & Bar‐Joseph, Z. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt222 · Cited by 20
“In their seminal article, Leung and Malik (2001) defined texture by convolving an image with a bank of 48 Gaussian filters and used the filter responses at each image location to define a ‘texton’.”
BackgroundPredicate Logic Based Image Grammars for Complex Pattern Recognition
Shet, V. D., Singh, M., Bahlmann, C., Ramesh, V., Neumann, J., & Davis, L. S. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0343-9 · Cited by 20
“Such configurations have been studied in literature as texture (with a texton being the micro structure) and identified as a significant feature for perception and identification, both in psychophysics (Julesz 1981) and computer vision (Leung and Malik 2001).”
BackgroundMethodDynamic Foreground/Background Extraction from Images and Videos using Random Patches
Lü, L., & Hager, G. D. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0121 · Cited by 20
OBJECT RECOGNITION BASED ON BAG OF FEATURES AND A NEW LOCAL PATTERN DESCRIPTOR
Ferraz, C. T., PEREIRA, O., ROSA, M. V., & Gonzaga, A. (2014) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001414550106 · Cited by 20
Adaptive Median Binary Patterns for Texture Classification
Hafiane, A., Palaniappan, K., & Seetharaman, G. S. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.205 · Cited by 20
“Methods developed over the last decade based on histograms of local primitives and related properties show remarkable accuracy, reliability and generality [3], [4], [5], [6], [7], [8], [9].”
BackgroundRandom forests for dura mater microvasculature segmentation using epifluorescence images
Kassim, Y. M., Prasath, V. B. S., Pelapur, R., et al. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591336 · Cited by 20
“sian (LoG) filters (2-D): We used a subset of 26 multiscale filters from the Leung-Malik 48-filter bank [11] consisting of 18 oriented second derivative (6 orientation, 3 scale, mean normalized) and 8 isotropic LoG filters.”
MethodMultivariate image analysis of realgar–orpiment flotation froths
Aldrich, C., Smith, L. K., Verrelli, D. I., Bruckard, W. J., & Kistner, M. (2017) · Mineral Processing and Extractive Metallurgy Transactions of the Institutions of Mining and Metallurgy · doi:10.1080/03719553.2017.1318570 · Cited by 20
“Textons can be defined as cluster centres in a filter response space that are computed through convolution of a training set of images with oriented linear spatial basis functions arranged in a filter bank (Leung & Malik 2001), as indicated diagrammatically in Figure 6.”
Talavera-Martínez, L., Bibiloni, P., & González-Hidalgo, M. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105049 · Cited by 20
Texture analysis of non-uniform images using GLCM
Saroja, G. A. S., & Sulochana, C. H. (2013) · IEEE Conference on Information and Communication Technologies (ICT) · doi:10.1109/cict.2013.6558306 · Cited by 20
Can, T., Karalı, A. O., & Aytaç, T. (2011) · Applied Optics · doi:10.1364/ao.50.006302 · Cited by 20
“We used the k-means algorithm [25–28] to cluster the target and background SIFT descriptors by using different k values for target and background.”
MethodPrincipal curvatures based rotation invariant algorithms for efficient texture classification
Hanbay, K., Alpaslan, N., Talu, M. F., et al. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.032 · Cited by 20
Cula, O. G., & Dana, K. (2002) · Cited by 20
Image-based control of skin melanin texture
Tsumura, N., Nakaguchi, T., Ojima, N., et al. (2006) · Applied Optics · doi:10.1364/ao.45.006626 · Cited by 20
Makeyev, O., Sazonov, E. E., Baydyk, T., & Martín, A. (2007) · Neurocomputing · doi:10.1016/j.neucom.2007.05.004 · Cited by 20
“Representative texture elements (textons) for texture description and recognition were proposed in [14].”
An empirical Bayes approach to contextual region classification
Lazebnik, S., & Raginsky, M. (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.2009.5206690 · Cited by 20
“We use a subset of the LM filter bank [7] consisting of 18 second-derivative-of-Gaussian and 6 Laplacian filters, and 13 filters from the S filter bank [17], for a total of 37 filters.”
MethodA survey on image retrieval performance of different bag of visual words indexing techniques
Mukherjee, J., Mukhopadhyay, J., & Mitra, P. (2014) · IEEE Students’ Technology Symposium · doi:10.1109/techsym.2014.6807922 · Cited by 19
“It is first used for texture recognition [13].”
MethodRobust Color Texture Features Under Varying Illumination Conditions
Kandaswamy, U., Adjeroh, D., Schuckers, S., et al. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2162400 · Cited by 19
“Leung and Malik [24] and Varma and Zisserman [25] have also suggested the use of 3-”
BackgroundMethodInfluentialLeung, M.-K., Pang, W., Fu, C., et al. (2007) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2007.1034 · Cited by 19
“Leung and Malik [33] developed the 3D texton concept as clusters of filter output of textures under different viewing and lighting configurations.”
BackgroundEvaluation of Texture Descriptors for Validation of Counterfeit Documents
Berenguel, A., Terrades, O. R., Lladós, J., et al. (2017) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.204 · Cited by 19
“Leung-Malik (LM) is a multi scale, multi orientation filter bank with 48 filters [20].”
Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
Zhang, Z., Li, D., Liu, S., et al. (2018) · Applied Sciences · doi:10.3390/app8050748 · Cited by 19
“, local binary patterns (LBP) [15], the bag-of-words (BoW) model [16], and the convolutional neural network (CNN) [17], generally achieve promising results when training and testing in the same feature space, while the performances degrade significantly when training and testing in different feature spaces, as shown i…”
BackgroundSemi-Supervised Robust Mixture Models in RKHS for Abnormality Detection in Medical Images
Kumar, N. A., & Awate, S. P. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2975958 · Cited by 19
“Our framework is generic to incorporate a variety of image features modeling geometric and photometric properties of image texture, including the classic texton-label histograms [8], [9] or filter banks learned using deep convolutional neural networks.”
MethodInfluentialExplaining Self-Supervised Image Representations with Visual Probing
Basaj, D., Oleszkiewicz, W., Sieradzki, I., Górszczak, M., Rychalska, B., Trzcinski, T. P., & Zieliński, B. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/82 · Cited by 19
“For this purpose, we could use the original definition of visual words from [Leung and Malik, 2001].”
MethodSudarshan, V. K., Acharya, U. R., Ng, E. Y. K., Tan, R., Chou, S. M., & Ghista, D. N. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.01.028 · Cited by 19
“Leung and Malik [50] are the first to propose a complete texture classification system based on textons [50].”
BackgroundDominant Texture and Diffusion Distance Manifolds
Lu, J., Dorsey, J. O., & Rushmeier, H. (2009) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2009.01407.x · Cited by 19
Statistical Approaches to Material Classification.
Varma, M., & Zisserman, A. (2002) · Cited by 19
Vision-Based Road Detection using Contextual BlocksPreprint
Mendes, C. C. T., Frémont, V., & Wolf, D. F. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.01122 · Cited by 19
“Entropy, Local Binary Patterns (LBP) and Leung-Malik (LM) [8] filters responses features are generated based on the grayscale image.”
BackgroundInvariant texture classification using a spatial filter bank in multi-resolution analysis
Ahmadvand, A., & Daliri, M. R. (2015) · Image and Vision Computing · doi:10.1016/j.imavis.2015.10.002 · Cited by 18
Mass classification in mammograms based on two-concentric masks and discriminating texton
Li, Y., Chen, H., Wei, X., et al. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.06.021 · Cited by 18
“These micro-structures can be captured by the response to a set of the filter banks [22] or by small neighborhood intensities [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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