@inbook{fcf6c54f27824410b3c4971973b56678,

title = "An Efficient Coding of Three Dimensional Colour Distributions for Image Retrieval",

abstract = "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.",

author = "J. Berens and Finlayson, {G. D.}",

year = "2002",

doi = "10.1007/3-540-45479-9_26",

language = "English",

isbn = "978-3-540-43899-1",

volume = "2383",

series = "Lecture Notes in Computer Science",

publisher = "Springer Berlin / Heidelberg",

pages = "245--252",

editor = "Lew, {Michael S.} and Nicu Sebe and Eakins, {John P.}",

booktitle = "Image and Video Retrieval",

note = "International Conference on Image and Video Retrieval ; Conference date: 18-07-2002 Through 19-07-2002",

}