Reduced complexity content-based image retrieval using vector quantization

We present a low complexity approach for content-based image retrieval (CBIR) using vector quantization (VQ). The VQ codebooks serve as generative image models and are used to represent images while computing their similarity. The hope is that encoding an image with a codebook of a similar image will yield a better representation than when a codebook of a dissimilar image is used. Experiments performed on a color image database support this hypothesis, and retrieval based on this method compares well with previous work. Our basic method "tags" each image with a thumbnail and a small VQ codebook of only 8 entries, where each entry is a 6 element color feature vector. In addition, we consider augmenting feature vectors with x-y coordinates associated with the entry

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