Robust color indexing

In content based image retrieval, color indexing is one of the most prevalent retrieval methods. In literature, most of the attention has been focussed on the color model with little or no consideration of the noise models. In this paper we investigate the problem of color indexing from a maximum likelihood perspective. We take into account the color model, the noise distribution, and the quantization of the color features. Furthermore, from the real noise distribution we derive a distortion measure, which consistently provides improved accuracy. Our investigation concludes with results on a real stock photography database, consisting of 11,000 color images.