Content-Based Image Retrieval Via Vector Quantization

Image retrieval and image compression are each areas that have received considerable attention in the past. In this work, we present an 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 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 by considering spatial relationships as well and retrieval based on this method compares well with previous work. To effectively utilize information stored in a digital image library, effective image indexing and retrieval techniques are essential. This paper proposes an image indexing and retrieval technique based on the compressed image data using vector quantization (VQ). By harnessing the characteristics of VQ, the proposed technique is able to capture the spatial relationships of pixels when indexing the image. Experimental results illustrate the robustness of the proposed technique and also show that its retrieval performance is higher compared with existing color-based techniques. One of the key roles of Vector Quantization (VQ) is to compress the data about the image database. In the past years, many improved algorithms of VQ codebook generation approaches have been developed. In this paper, we present a snapshot of the recent developed schemes. The discussed schemes include LBG, enhance LBG (ELBG).