Image retrieval using color histograms generated by Gauss mixture vector quantization
| Autor: | Robert M. Gray, Sangoh Jeong, Chee Sun Won |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Color histogram
Color image business.industry Color normalization Quantization (signal processing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Vector quantization Pattern recognition Data_CODINGANDINFORMATIONTHEORY Color space Color quantization Computer Science::Computer Vision and Pattern Recognition Signal Processing Color depth Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Software Mathematics |
| Zdroj: | Computer Vision and Image Understanding. 94:44-66 |
| ISSN: | 1077-3142 |
| DOI: | 10.1016/j.cviu.2003.10.015 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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