Image retrieval using color histograms generated by Gauss mixture vector quantization

Authors:
Sangoh Jeong;Chee Sun Won;Robert M. Gray
Affiliations:
Department of Electrical Engineering, Stanford University, Stanford, CA;Department of Electronic Engineering, Dongguk University, Seoul, 100-715, Republic of Korea;Department of Electrical Engineering, Stanford University, Stanford, CA
Venue:
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Year:
2004

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Abstract

Image retrieval based on color histograms requires quantization of a color space. Uniform scalar quantization of each color channel is a popular method for the reduction of histogram dimensionality. With this method, however, no spatial information among pixels is considered in constructing the histograms. Vector quantization (VQ) provides a simple and effective means for exploiting spatial information by clustering groups of pixels. We propose the use of Gauss mixture vector quantization (GMVQ) as a quantization method for color histogram generation. GMVQ is known to be robust for quantizer mismatch, which motivates its use in making color histograms for both the query image and the images in the database. Results show that the histograms made by GMVQ with a penalized log-likelihood (LL) distortion yield better retrieval performance for color images than the conventional methods of uniform quantization and VQ with squared error distortion.