A comparative study of color-texture image features

In this work we compare two spatial and two wavelet-domain feature extraction methods that have been proposed in the recent literature for color-texture classification. The corresponding color-texture features, namely the Opponent-Color Local Binary Pattern distributions, the Chromaticity Moments, the Wavelet Correlation Signatures and the Color Wavelet Covariance features, are extracted in RGB, I1I2I3, HSV and CIE-Lab color spaces. The classification task is realized by Support Vector Machines. Experiments are performed on two standard datasets comprising of 54 and 68 textures from the Vistex and the Outex databases respectively. The results show that in most cases color enhances texture classification. Both spatial and wavelet features can lead to an accurate representation of color-textures. The appropriateness of a color-texture feature extraction method has to be determined by considering the trade-off between the accuracy and the feature space dimensionality needs, as these are imposed by a prospective application.