Image decomposition with anisotropic diffusion applied to leaf-texture analysis
This work addresses texture classification problems for computer vision applications, particularly in leaf-texture analysis, with incremental improvements.
The authors tackled texture analysis by proposing a novel PDE-based approach that decomposes images into components using anisotropic diffusion and computes Gabor features, achieving higher classification rates on texture datasets and leaf-texture analysis.
Texture analysis is an important field of investigation that has received a great deal of interest from computer vision community. In this paper, we propose a novel approach for texture modeling based on partial differential equation (PDE). Each image $f$ is decomposed into a family of derived sub-images. $f$ is split into the $u$ component, obtained with anisotropic diffusion, and the $v$ component which is calculated by the difference between the original image and the $u$ component. After enhancing the texture attribute $v$ of the image, Gabor features are computed as descriptors. We validate the proposed approach on two texture datasets with high variability. We also evaluate our approach on an important real-world application: leaf-texture analysis. Experimental results indicate that our approach can be used to produce higher classification rates and can be successfully employed for different texture applications.