Image indexing using compressed colour histograms
The distribution of colours in an image has proven to be very useful for object recognition. Building on Swain's colour indexing, colour distributions are now an integral part of many recognition schemes. This is not to say that colour alone suffices but rather that colour is one important cue that aids recognition. Colour indexing is performed on colour distribution histograms, and as such, the speed of the system is directly related to the size of the histogram to be indexed. It is shown how colour histograms can be effectively compressed and how compressed colour histograms can be compared for indexing. The authors make two important contributions. First, they show that an opponent colour histogram can be compressed more readily than can conventional colour space. Secondly, they use the standard transform encoding methods (the Karhunen-Loeve transform, the discrete cosine transform, the Hadamard transform and hybrid transforms) to compress colour histograms. Experiments show that compression rates of up to 250:1 are possible without affecting indexing performance. This means that a database can be searched that is 250 times larger in the same time as that searched by conventional indexing.