Copyright © 2003 Elsevier Inc. All rights reserved.
Image retrieval using color histograms generated by Gauss mixture vector quantization
Received 1 December 2002;
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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
- 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
- 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







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