@inproceedings{bdedcfa3b51c4621a7f3ae0b0d8a9238,
title = "Histogram-based image retrieval using Gauss mixture vector quantization",
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.",
author = "Sangoh Jeong and Won, {Chee Sun} and Gray, {R. M.}",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; 2003 International Conference on Multimedia and Expo, ICME 2003 ; Conference date: 06-07-2003 Through 09-07-2003",
year = "2003",
doi = "10.1109/ICME.2003.1221637",
language = "English",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
pages = "397--400",
booktitle = "Proceedings - 2003 International Conference on Multimedia and Expo, ICME",
address = "United States",
}