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Texture analysis is an important field of investigation that has received agreat deal of interest from computer vision community. In this paper, wepropose a novel approach for texture modeling based on partial differentialequation (PDE). Each image $f$ is decomposed into a family of derivedsub-images. $f$ is split into the $u$ component, obtained with anisotropicdiffusion, and the $v$ component which is calculated by the difference betweenthe original image and the $u$ component. After enhancing the texture attribute$v$ of the image, Gabor features are computed as descriptors. We validate theproposed approach on two texture datasets with high variability. We alsoevaluate our approach on an important real-world application: leaf-textureanalysis. Experimental results indicate that our approach can be used toproduce higher classification rates and can be successfully employed fordifferent texture applications.

Image decomposition with anisotropic diffusion applied to leaf-texture analysis