Histogram-based image retrieval using Gauss mixture vector quantization

Sangoh Jeong, Chee Sun Won, R. M. Gray

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Histogram-based image retrieval requires some form of quantization since the raw color images result in large dimensionality in the histogram representation. Simple uniform quantization disregards the spatial information among pixels in making histograms. Since traditional vector quantization (VQ) with squared-error distortion employs only the first moment, it neglects the relationship among vectors. We propose Gauss mixture vector quantization (GMVQ) as the quantization method for a histogram-based image retrieval to capture the spatial information in the image via the Gaussian covariance structure. Two common histogram distance measures are used to evaluate the similarity of histograms resulting from GMVQ. Our result shows that GMVQ with a quadratic discriminant analysis (QDA) distortion outperforms the two typical quantization methods in the histogram-based image retrieval.

Original languageEnglish
Title of host publicationProceedings - 2003 International Conference on Multimedia and Expo, ICME
PublisherIEEE Computer Society
Pages397-400
Number of pages4
ISBN (Electronic)0780379659
DOIs
StatePublished - 2003
Event2003 International Conference on Multimedia and Expo, ICME 2003 - Baltimore, United States
Duration: 6 Jul 20039 Jul 2003

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2003 International Conference on Multimedia and Expo, ICME 2003
Country/TerritoryUnited States
CityBaltimore
Period6/07/039/07/03

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