Approaches to color- and texture-based image classification

Abstract

A Gabor filtering method for texture-based classification of color images is presented. The algorithm is robust and can be used with different color representations. It involves a filter selection process based on texture smoothness. Unichannel and interchannel correlation features are computed. Two types of color representations have been considered: computing chromaticity values from xyY, HIS, and RGB spaces; and using the three channels of the perceptually uniform color spaces L*a*b* and HSV. The RGB space universally used in image processing can be used for color-texture-based classification by computing the rgb chromaticity values, which yield higher classification accuracies than the direct use of R, G, and B values. The wavelet transform methods have been extended to perform color image classifications with additional features. The two approaches, Gabor filtering and wavelet transform methods, are compared in terms of classification accuracy and efficiency. The pyramid wavelet transform (PWT) performs well with all color spaces. The tree-structured wavelet transform (TWT) is more suitable for smaller classification problems. The best color spaces for higher class problems with wavelet methods are L*a*b* and HSV spaces. The HSV space is found to be the best for application of both of these texture-based approaches. Computationally the Gabor method followed by PWT is fast and efficient.