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Compact Interchannel Sampling Difference Descriptor for Color Texture Classification.
- Source :
- IEEE Transactions on Circuits & Systems for Video Technology; May2021, Vol. 31 Issue 5, p1684-1696, 13p
- Publication Year :
- 2021
-
Abstract
- Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution, t-distribution to generate sample points in the image patch to calculate the micro-block difference. Then we model the interchannel relationship of color texture image by using dense micro-block differences. Furthermore, we utilize principal component analysis (PCA) to reduce the dimensions of the features encoded by the Fisher vector, and construct a Compact Interchannel Sampling Difference Descriptor (CISDD) for representing color texture image. Finally, experimental results on five published standard texture datasets (KTH-TIPS, VisTex, CUReT, USPTex and Colored Brodatz) reveal that CISDD is effective and outperforms thirteen representative color texture classification methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- PRINCIPAL components analysis
TEXTURES
IMAGE color analysis
MICROFLUIDICS
Subjects
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 31
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 150190037
- Full Text :
- https://doi.org/10.1109/TCSVT.2020.3014526