Color-based image retrieval using vector quantization and multivariate graph matching

A novel strategy for color-based image retrieval is introduced. Initially, a vector quantization technique is adopted, based on the application of self-organizing neural networks. The color content in each image is summarized by representative RGB-vectors extracted using the Neural-Gas network, an efficient way to extract faithful representations from multivariate distributions. The similarity between two images is then assessed as commonality between the corresponding representative color distributions and quantified using the multivariate Wald-Wolfowitz test, a nonparametric statistical test dealing with the "multivariate two-sample problem". Experimental results drawn from a diverse collection of color images show a significantly improved performance relative to the popular approach of color histogram.