Efficient and effective online image retrieval

With visual information becoming increasingly important, efficient and effective methods for querying and retrieving this kind of information are highly sought after. In this paper, we focus on image information and querying from image collections in an online retrieval fashion. In online retrieval, image features for performing retrieval are not pre-calculated but need to be extracted during the retrieval stage. Consequently, and in particular for large image datasets, the time required for feature extraction becomes crucial so as to not exceed interactive retrieval speeds. Our aim in this work is to match the retrieval accuracy of a high performing yet rather slow image retrieval method, but perform the retrieval in only a fraction of the time. We achieve this by a combination of two carefully crafted filtering stages both of which are based on the way data is stored in JPEG compressed images. The first of these performs extremely fast image retrieval using solely information contained in the JPEG headers. The second stage employs a compressed domain retrieval method that utilises features calculated from JPEG coefficient data. The first filter discards a large part of irrelevant images in a very fast fashion. The remaining images are filtered by the second technique in order to arrive at a relatively small subset of the complete database in a timely fashion. Finally, on this subset the high performing algorithm of choice, the MPEG-7 colour structure descriptor in this paper, is applied to produce a final ranking of the images to be returned to the user. Our experimental results demonstrate that on a large dataset of over 25,000 images our approach achieves retrieval scores nearly identical to those of the high performing technique while reducing the overall retrieval time by a factor of 15.