Effective and Efficient Filtering of Retrieved Images Based on JPEG Header Information

Visual information on the web, in particular in form of images, is increasing at a rapid rate. Consequently, efficient and effective techniques to retrieve visual information are sought after, especially as it can be usefully employed to augment textual information. Since users rarely annotate images, this proves to be a challenging task, however much progress has been reported in the area of content-based image retrieval which is based on visual features extracted from images for retrieval purposes. In this paper, we present two strategies for very fast image retrieval which use solely information contained in the header of JPEG compressed files. One is based on the tables that are responsible for the lossy quantisation step in JPEG, while the other is related to the Huffman tables used for entropy coding. In both cases, we employ the tables directly as image features in the context of online image retrieval. We then utilise them to discard irrelevant images, while a compressed-domain image retrieval technique is used for ranking the remaining image set. Experimental results convincingly show that our algorithms lead to a significant reduction of overall retrieval time while maintaining retrieval accuracy. They could thus be integrated into web-based recommender systems to augment and improve search results.