Discriminant Feature Selection for Texture Classification

The computational complexity of a texture classification algorithm is limited by the dimensionality of the feature space. Although finding the optimal feature subset is a NP-hard problem [1], a feature selection algorithm that can reduce the dimensionality of problem is often desirable. In this paper, we report work on a feature selection algorithm for texture classification using two subband filtering methods: a full wavelet packet decomposition and a Gabor type decomposition. The value of a cost function associated with a subband (feature) is used as a measure of relevance of that subband for classification purposes. This leads to a fast feature selection algorithm which ranks the features according to their measure of relevance. Experiments on a range of test images and both filtering methods provide results that are promising.