, a nonuniform color space, is almost universally accepted by the image processing community as the means for representing color. On the other hand, perceptually uniform spaces, such as , as well as approximately-uniform color spaces, such as , exist, in which measured color differences are proportional to the human perception of such differences. This paper compares with and in terms of their effectiveness in color texture analysis. There has been a limited but increasing amount of work on the color aspects of textured images recently. The results have shown that incorporating color into a texture analysis and recognition scheme can be very important and beneficial. The presented methodology uses a family of Gabor filters specially tuned to measure specific orientations and sizes within each color texture. Effectiveness is measured by classification performance of each color space, as well as by classifier-independent measures. Experimental results are obtained with a variety of color texture images. Percuptually uniform spaces are shown to outperform in many cases.

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A number of different approaches have been recently presented for image retrieval using color features. Most of these methods use the color histogram or some variation of it. If the extracted information is to be stored for each image, such methods may require a significant amount of space for storing the histogram, depending on a given image's size and content. In the method proposed in this paper, only a small number of features, called chromaticity moments, are required to capture the spectral content (chrominance) of an image. The proposed method is based on the concept of the chromaticity diagram and extracts a set of twodimensional moments from it to characterize the shape and distribution of chromaticities of the given image. This representation is compact (only a few chromaticity moments per image are required) and constant (independent of image size and content), while its retrieval effectiveness is comparable to using the full chromaticity histogram.

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