An Efficient Coding of Three Dimensional Colour Distributions for Image Retrieval

J. Berens, G. D. Finlayson

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationImage and Video Retrieval
EditorsMichael S. Lew, Nicu Sebe, John P. Eakins
PublisherSpringer Berlin / Heidelberg
Number of pages8
ISBN (Print)978-3-540-43899-1
Publication statusPublished - 2002
EventInternational Conference on Image and Video Retrieval - London, United Kingdom
Duration: 18 Jul 200219 Jul 2002

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg


ConferenceInternational Conference on Image and Video Retrieval
Country/TerritoryUnited Kingdom

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