Singular value decomposition for texture analysis
Jen-Hon Luo, Chien-Chang Chen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
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.