research-article

Nonnegative Multiresolution Representation-Based Texture Image Classification

Authors:

Yongsheng Dong,

Xuelong LiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 7, Issue 1

Article No.: 4, Pages 1 - 21

https://doi.org/10.1145/2738050

Published: 07 October 2015 Publication History

Abstract

Effective representation of image texture is important for an image-classification task. Statistical modelling in wavelet domains has been widely used to image texture representation. However, due to the intraclass complexity and interclass diversity of textures, it is hard to use a predefined probability distribution function to fit adaptively all wavelet subband coefficients of different textures. In this article, we propose a novel modelling approach, Heterogeneous and Incrementally Generated Histogram (HIGH), to indirectly model the wavelet coefficients by use of four local features in wavelet subbands. By concatenating all the HIGHs in all wavelet subbands of a texture, we can construct a nonnegative multiresolution vector (NMV) to represent a texture image. Considering the NMV’s high dimensionality and nonnegativity, we further propose a Hessian regularized discriminative nonnegative matrix factorization to compute a low-dimensional basis of the linear subspace of NMVs. Finally, we present a texture classification approach by projecting NMVs on the low-dimensional basis. Experimental results show that our proposed texture classification method outperforms seven representative approaches.

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Index Terms

Nonnegative Multiresolution Representation-Based Texture Image Classification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
  2. Machine learning

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 7, Issue 1

October 2015

293 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2830012

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2015 ACM.

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Publication History

Published: 07 October 2015

Accepted: 01 February 2015

Revised: 01 February 2015

Received: 01 November 2014

Published in TIST Volume 7, Issue 1

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China Post-Doctoral Science Foundation
Program for Innovative Research Team (in Science and Technology) in University of Henan Province
Key Science and Technology Research Project of Henan Provinces Education Department of China
National Natural Science Foundation of China
Key Research Program of the Chinese Academy of Sciences

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https://doi.org/10.1109/TCYB.2019.2903591
Dong YJin MLi XMa JLiu ZWang LZheng L(2021)Compact Interchannel Sampling Difference Descriptor for Color Texture ClassificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.301452631:5(1684-1696)Online publication date: May-2021
https://doi.org/10.1109/TCSVT.2020.3014526
Johari NMamat MChekima A(2021)Performance of Machine Learning Classifiers in Distress Keywords Recognition for Audio Surveillance Applications2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)10.1109/IICAIET51634.2021.9573852(1-5)Online publication date: 13-Sep-2021
https://doi.org/10.1109/IICAIET51634.2021.9573852
Karimi MMajidi MMirSaeedi HArefi MOskuoee M(2020)A Novel Application of Deep Belief Networks in Learning Partial Discharge Patterns for Classifying Corona, Surface, and Internal DischargesIEEE Transactions on Industrial Electronics10.1109/TIE.2019.290858067:4(3277-3287)Online publication date: Apr-2020
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Jin MDong YZhang MWu QZheng LSong BZhang LWang L(2020)Completed Extremely Nonnegative DMD for Color Texture ClassificationIEEE Access10.1109/ACCESS.2020.29989268(103034-103046)Online publication date: 2020
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Dong YZhang HLiu ZYang CXie GZheng LWang L(2019)Neutrosophic Set Transformation Matrix Factorization Based Active Contours for Color Texture SegmentationIEEE Access10.1109/ACCESS.2019.29284157(93887-93897)Online publication date: 2019
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