Sorting JPEG images at a glance

While content-based image retrieval (CBIR) has been an active research area for more than two decades, the computational overhead associated with image feature extraction is often high, making existing methods unsuitable for on-line retrieval where image features need to be extracted during the retrieval process. In this paper, we present an image retrieval algorithm for JPEG images that works in an extremely fast fashion, and is based solely on information contained in the file headers. In particular, we demonstrate that optimising the Huffman tables of JPEG files not only leads to improved compression but also allows retrieval based on the (image adapted) Huffman tables. Exploiting this leads to a retrieval method that is about 40 times faster than existing compressed domain algorithms and at least 150 times faster than common pixel-domain methods. While retrieval performance on its own does not quite match that of current techniques, the method is shown to work well as an image filter to discard a large part of a database in an efficient way. Combined with a more accurate compressed-domain retrieval algorithm it is found that retrieval time can be shortened by about 80% without sacrificing retrieval accuracy.