Content-based image retrieval via vector quantization

Image retrieval and image compression are each areas that have received considerable attention in the past. However there have been fewer advances that address both these problems simultaneously. In this work, we present a novel approach for content-based image retrieval (CBIR) using vector quantization (VQ). Using VQ allows us to retain the image database in compressed form without any need to store additional features for image retrieval. 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 over a range of codebook sizes support this hypothesis and retrieval based on this method compares well with previous work.