Color space selection and quantization are critical to content-based image retrieval based on color histograms. In this work, we first examine the color distribution in different color spaces including RGB, HSV, YUV and Munsell spaces and discuss the appropriate quantization strategies in these color spaces based on the distribution of colors. Then, we propose a color quantization scheme which applies the LloydMax quantizer along each axis independently. The proposed scheme is simple yet efficient. Retrieval using the proposed quantization scheme on difference color spaces are compared through experiments.

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The application of multiresolution colore quantization and indexing schemes to color-based image retrieval is investigated in this research. We first perform a thorough comparison of different quantization schemes in RGB, HSV, YUV and CIELUV color spaces. Then, a new feature based on the octree structure of colore quantization is proposed to achieve efficient multiresolution image indexing and retrieval. Extensive experiments are performed to illustrate the performance of the proposed multiresolution retrieval approach.

After performing a thorough comparison of different quantization schemes in the RGB; H SV; Y U V; and C I EL 3 u 3 v 3color spaces, we propose to use color features obtained by hierarchical color clustering based on a pruned octree data structure to achieve efficient and robust image retrieval. With the proposed method, multiple color features, including the dominant color, the number of distinctive colors, and the color histogram, can be naturally integrated into one framework. A selective filtering strategy is also described to speed up the retrieval process. Retrieval examples are given to illustrate the performance of the proposed approach.Index Terms-Color quantization, content-based retrieval, image database, image indexing, image retrieval, query processing.