Improving Texture Categorization with Biologically Inspired Filtering

Abstract

International audienceWithin the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms.However, preprocessing techniques have received much less attention despite their important potential for improving the overall classification performance. We address this question by proposing a novel, simple, yet very powerful biologically-inspiredfiltering (BF) which simulates the performance of human retina. In the proposed approach, given a texture image, after applying a difference of Gaussian (DoG) filter to detect the edges, we first split the filtered image into two maps alongside the sides ofits edges. The feature extraction step is then carried out on the two maps instead of the input image. Our algorithm has several advantages such as simplicity, robustness to illumination and noise, and discriminative power. Experimental results on three largetexture databases show that with an extremely low computational cost, the proposed method improves significantly the performance of many texture classification systems, notably in noisy environments