International Journal of Computer Vision
Searching Multimedia Databases by Content
Searching Multimedia Databases by Content
IEEE Transactions on Pattern Analysis and Machine Intelligence
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
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The distribution of colours in an image provides a useful cue for image indexing and object recognition [1,3,2,4]. Previously, we have shown how chromaticity distributions can be coded using a hybrid compression technique: histograms are coded with a Discrete Cosine Transform and then Principal Component Analysis is applied to a reduced set of the DCT coefficients, resulting in excellent indexing results, using just the first eight Principal Components [5,6]. We have investigated compression on colour distributions independent of colour intensity, however, colour is generally represented by a 3-D model, (two chromaticity channels and one intensity channel). One difficulty with 3-D chromaticity distribution histograms is their sparseness - many bins contain no or few image pixels. This becomes a problem when attempting to derive PCA statistics: it becomes necessary to analyse an unrealistically large number of histograms. We show that applying the Discrete Fourier Transform to colour distribution histograms leads to a dimensionality reduction that makes PCA possible. We also demonstrate the general case that 3-D and n-D distributions, particularly sparse ones, can be significantly reduced in dimension.