Log-opponent chromaticity coding of colour space

The distribution of colours in an image often provides a useful cue for image indexing and object recognition. However, two problems are reported in the literature: firstly, colour distributions are dependent on the illumination colour, and secondly, that colour distributions represented as histograms are large in size thus limiting the scale of the database that might reasonably be indexed. Both of these problems have been separately addressed in the literature. But, the derived solutions are not compatible with one another. We look at both problems together and at the same time we develop a parsimonious representation which consists of distinct illuminant dependent and independent parts. Our representation is based on a log-opponent chromaticity representation. By using chromaticities we avoid the problem of brightness indeterminancy. Opponency gives a perceptually relevant and efficient coding. Finally, the use of logarithms renders illuminant change simple to model: as the illumination changes, so the distribution of log-opponent chromaticities undergo a simple translation. We code log-opponent chromaticity distributions by the distribution mean and the lowest k statistical moments. We show that only the mean in this expansion depends on illumination. Experiments show two important results-indexing using both mean and as few as 8 moments delivers near perfect indexing for an illuminant colour corrected database, while indexing without the mean delivers near perfect indexing for Funt et al's illuminant dependent images.