COLOR CONTENT DESCRIPTORS OF IMAGES BY VECTOR QUANTIZATION.

In the paper, we propose color content descriptors of images by vector quantization in RGB color space compared to scalar ones. In order to obtain a much more accurate discrete representation of this space we use our algorithm for optimization of vector quantizers. We introduce several modifications of these descriptors such as global, structural as well as of dominant color. We consider a different number of bins and evaluated the similarity of the color content of images using mean square error of color histograms of a reference image and a searched image. Then, the color content of the image with the minimum error was the most similar to the reference image. We also used a parameter of variance, if the color content of several searched images was very similar based on the mean quadratic error for the structural descriptor. By vector quantization of RGB space we can achieve 2-3 times decreasing the number of bins at the same accuracy. [ABSTRACT FROM AUTHOR]