Histogram-based image retrieval using Gauss mixture vector quantization

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 results show that GMVQ, with a quadratic discriminant analysis (QDA) distortion, outperforms the two typical quantization methods in histogram-based image retrieval.