Approaches to color- and texture-based image classification

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: (1) computing chromaticity values from xyY, HIS and RGB spaces and (2) using the 3 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 rgbchromaticity 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