Iterative Feature Selection for Color Texture Classification

Abstract

In this paper, we describe a new approach for color texture classification by use of Haralick features extracted from color co-occurrence matrices. As the color of each pixel can be represented in different color spaces, we automatically determine in which color spaces, these features are most discriminating for the textures. The originality of this approach is to select the most discriminating color texture features in order to build a feature space with a low dimension. Our method, based on a supervised learning scheme, uses an iterative selection procedure. It has been applied and tested on the BarkTex benchmark database.