article

Discriminative features for texture description

Authors:

Matti PietikäInenAuthors Info & Claims

Pattern Recognition, Volume 45, Issue 10

Pages 3834 - 3843

https://doi.org/10.1016/j.patcog.2012.04.003

Published: 01 October 2012 Publication History

Abstract

In this paper, a feature extraction method is developed for texture description. To obtain discriminative patterns, we present a learning framework which is formulated into a three-layered model. It can estimate the optimal pattern subset of interest by simultaneously considering the robustness, discriminative power and representation capability of features. This model is generalized and can be integrated with existing LBP variants such as conventional LBP, rotation invariant patterns, local patterns with anisotropic structure, completed local binary pattern (CLBP) and local ternary pattern (LTP) to derive new image features for texture classification. The derived descriptors are extensively compared with other widely used approaches and evaluated on two publicly available texture databases (Outex and CUReT) for texture classification, two medical image databases (Hela and Pap-smear) for protein cellular classification and disease classification, and a neonatal facial expression database (infant COPE database) for facial expression classification. Experimental results demonstrate that the obtained descriptors lead to state-of-the-art classification performance.

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Cited By

Luo YSa JSong YJiang HZhang CZhang Z(2023)Texture classification combining improved local binary pattern and threshold segmentationMultimedia Tools and Applications10.1007/s11042-023-14749-882:17(25899-25916)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-14749-8
Tabatabaei SChalechale A(2022)Median arc center corrected binary pattern (MACCBP) for noise robust feature extractionMultidimensional Systems and Signal Processing10.1007/s11045-022-00848-633:4(1409-1444)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11045-022-00848-6
Mitra SDas NDey SChakraborty SNasipuri MNaskar M(2021)Cytology Image Analysis Techniques Toward AutomationACM Computing Surveys10.1145/344723854:3(1-41)Online publication date: 17-Apr-2021
https://dl.acm.org/doi/10.1145/3447238
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Discriminative features for texture description
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 45, Issue 10

October, 2012

271 pages

ISSN:0031-3203

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Copyright © Elsevier Ltd © 2012.

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Elsevier Science Inc.

United States

Publication History

Published: 01 October 2012

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Cited By

Luo YSa JSong YJiang HZhang CZhang Z(2023)Texture classification combining improved local binary pattern and threshold segmentationMultimedia Tools and Applications10.1007/s11042-023-14749-882:17(25899-25916)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-14749-8
Tabatabaei SChalechale A(2022)Median arc center corrected binary pattern (MACCBP) for noise robust feature extractionMultidimensional Systems and Signal Processing10.1007/s11045-022-00848-633:4(1409-1444)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11045-022-00848-6
Mitra SDas NDey SChakraborty SNasipuri MNaskar M(2021)Cytology Image Analysis Techniques Toward AutomationACM Computing Surveys10.1145/344723854:3(1-41)Online publication date: 17-Apr-2021
https://dl.acm.org/doi/10.1145/3447238
Song TFeng JLuo LGao CLi H(2021)Robust Texture Description Using Local Grouped Order Pattern and Non-Local Binary PatternIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297215531:1(189-202)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TCSVT.2020.2972155
Asif MWang JGao YZhou J(2021)Composite description based on color vector quantization and visual primary features for CBIR tasksMultimedia Tools and Applications10.1007/s11042-021-11353-680:24(33409-33427)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s11042-021-11353-6
Anbari MFotouhi A(2021)Finger knuckle print recognition for personal authentication based on relaxed local ternary pattern in an effective learning frameworkMachine Vision and Applications10.1007/s00138-021-01178-632:3Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1007/s00138-021-01178-6
Zhao CLv XZhang ZZuo WWu JMiao D(2020)Deep Fusion Feature Representation Learning With Hard Mining Center-Triplet Loss for Person Re-IdentificationIEEE Transactions on Multimedia10.1109/TMM.2020.297212522:12(3180-3195)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1109/TMM.2020.2972125
Xiao BWang KBi XLi WHan J(2019)2D-LBP: An Enhanced Local Binary Feature for Texture Image ClassificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2018.286984129:9(2796-2808)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1109/TCSVT.2018.2869841
Tavakolian MHadid A(2019)A Spatiotemporal Convolutional Neural Network for Automatic Pain Intensity Estimation from Facial DynamicsInternational Journal of Computer Vision10.1007/s11263-019-01191-3127:10(1413-1425)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11263-019-01191-3
Geetha ASundaram MVijayakumari B(2019)RETRACTED ARTICLE: Gender classification from face images by mixing the classifier outcome of prime, distinct descriptorsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-03679-523:8(2525-2535)Online publication date: 1-Apr-2019
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