Statistical analysis of textures from compressed images

This paper is devoted to a statistical analysis of textures from two codings. This analysis discriminates textures: a classification is built, not directly upon texture images, but upon a compressed information, which is created from each texture and composed of two coding images. The principle of this analysis is as follows: a texture images set is used. Each texture, composed of 256 gray levels, is encoded by two coding images of 15 colors. Some Kolmogorov-Smirnov's tests are carried out on combinations of two coding images in view to get a first discrimination. In the same time, from each coding image, co-occurrence parameters are computed. These parameters are used, with the previous discrimination, to get classifications. These classifications are compared to another one made only with co- occurrence parameters directly computed from a basis-textures set. In conclusion, we consider advantages and drawbacks of our approach and perspectives for the future.

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