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Computer Vision and Image Understanding
Volume 94, Issues 1-3, April-June 2004, Pages 44-66
Special Issue: Colour for Image Indexing and Retrieval
 
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doi:10.1016/j.cviu.2003.10.015    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2003 Elsevier Inc. All rights reserved.

Image retrieval using color histograms generated by Gauss mixture vector quantization

Sangoh Jeong Corresponding Author Contact Information, E-mail The Corresponding Author, a, Chee Sun Won E-mail The Corresponding Author, b and Robert M. Gray E-mail The Corresponding Author, a

a Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA b Department of Electronic Engineering, Dongguk University, Seoul, 100-715, Republic of Korea

Received 1 December 2002; 
accepted 29 October 2003. 
Available online 23 December 2003.

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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.

Article Outline

1. Introduction
2. Quantization of color spaces for histogram generation
2.1. Scalar quantization
2.2. Vector quantization
2.3. Gauss mixture vector quantization
2.3.1. Gauss mixture model
2.3.2. Lloyd algorithm for density estimation of a Gauss mixture model
2.3.3. Robustness of the GMVQ
3. Histogram generation
4. Histogram distance measures
4.1. Histogram Euclidean (HE) distance
4.2. Histogram intersection (HI) distance
4.3. Histogram quadratic distance
5. Implementation
5.1. Image database
5.2. Quantization methods
5.2.1. SQ: uniform quantization
5.2.2. MSE VQ: traditional GLA
5.2.3. GMVQ: vector quantization using Gauss mixture model
6. Results
6.1. Selection of a color space and histogram distance measures
6.2. Comparison of retrieval performances based on quantization methods
6.3. Robustness of the GMVQ in image retrieval
6.4. Retrieval performances using 4×4 vectors in GMVQ
7. Concluding remarks
Acknowledgements
References














Computer Vision and Image Understanding
Volume 94, Issues 1-3, April-June 2004, Pages 44-66
Special Issue: Colour for Image Indexing and Retrieval
 
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