Using Colour for Image Indexing

Image colour is often thought to be an intrinsic correlate of surface re ectance and so is a common feature for image indexing. In this paper we point out that image colour is actually a function of surface re ectance and imaging geometry and the colour of the viewing illuminant. Fortunately methods exist for normalizing away these dependencies. Pixel based and colour channel-based normalizations remove dependency on geometry and light colour respectively. Unfortunately, neither method removes both dependencies simultaneously and so a single normalization must be chosen. Common practice dictates that pixel-based normalization is the most useful. In this paper we set out to evaluate the merits or demerits of this common practice. In particular, we asked `which works better, pixel based or channel based normalizations?'. To answer this question we carried our many image indexing experiments on a variety of image databases. In all cases, and contrary to common practice, our results indicate that channel normalization facilitates the best indexing performance. We predict that channel based normalization may improve indexing performance for many image indexing applications.