An efficient low-dimensional color indexing scheme for region-based image retrieval

In this work, an efficient low-dimensional color indexing scheme for region-based image retrieval is presented. The colors in each image region are first quantized so that only a small number of cluster centroids are needed to represent the region color information. The proposed color feature descriptor consists of these quantized colors and their percentages in the region. A similarity distance measure is defined and shown to be equivalent to the quadratic color histogram distance measure. The quantized colors are indexed in the 3-D color space so that high-dimensional indexing can be avoided. During the search process, each quantized color in the query is used as a separate cue to find matches containing that color. The matches from all the query colors are then joined to obtain the final retrievals. Experimental results show that the proposed scheme is fast and accurate compared to the color histogram approach.