Singular value decomposition for texture analysis

Jen-Hon Luo; Chaur-Chin Chen

doi:10.1117/12.186553

21 September 1994 Singular value decomposition for texture analysis

Jen-Hon Luo, Chaur-Chin Chen

Proceedings Volume 2298, Applications of Digital Image Processing XVII; (1994) https://doi.org/10.1117/12.186553
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

Texture is an important characteristic of analyzing images. A variety of texture features have been proposed for texture discrimination, whereas a best set of texture features never exists. This paper considers statistical textures which can be viewed as realizations of some stochastic processes, or viewed as images containing no apparent objects. We propose using singular value decomposition (SVD) strategy for texture analysis including (a) using the proportion of dominant singular values of an image matrix as texture features for texture discrimination, (b) the singular value decomposition automatically provides a compression technique for textures due to the dependency of neighboring pixels, and (c) an algorithm based on SVD is proposed to synthesize textures. The texture features derived from SVD are stable according to the stability of SVD. Experiments for discriminating synthesized textures and natural textures, for compressing texture data and for synthesizing textures are also given to demonstrate the proposed strategy.

Citation Download Citation

Jen-Hon Luo and Chaur-Chin Chen "Singular value decomposition for texture analysis", Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); https://doi.org/10.1117/12.186553

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