Comparison of feature selection schemes for color texture classification

Abstract

In this paper, we propose to compare the performances of two sequential feature selection schemes used for supervised color texture classification. We focus this study on the sequential forward selection (SFS) scheme and the more complex sequential forward floating selection (SFFS) scheme which avoids the “nesting effect”. These schemes retain Haralick features extracted from chromatic co-occurrence matrices of images coded in different color spaces. We experimentally study the contribution of these two feature selection schemes with three benchmark color texture databases.