Texture Retrieval Based on Nonlocal Singular Value Decomposition and Multiscale Transforms

DOI

10.2991/eame-15.2015.177How to use a DOI?

Keywords

texture retrieval; NL-SVD; multiscale transforms; feature extraction

Abstract

Feature extraction based on the multiscale transforms is usually accomplished in the high frequency subbands. The features which are captured from the high frequency subbands mainly contain the edge changing information of images; meanwhile the structural information of images is mainly existed in the low frequency subband. In order to improve the effect of image processing, the structural features which are very important should be obtained from images. This paper adopts a nonlocal singular value decomposition (NL-SVD) algorithm to extract structural features from the low frequency subband. The structural features are combined with traditional statistical features such as means and standard deviations. These features are applied for texture retrieval. Experimental results show the retrieval ratio with these combined features is better and it proves that structural features are efficient texture features for images.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - J.W. Zhang
AU  - B. Liu
PY  - 2015/07
DA  - 2015/07
TI  - Texture Retrieval Based on Nonlocal Singular Value Decomposition and Multiscale Transforms
BT  - Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
PB  - Atlantis Press
SP  - 644
EP  - 647
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-15.2015.177
DO  - 10.2991/eame-15.2015.177
ID  - Zhang2015/07
ER  -