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Fast mobile image retrieval

Image collections, in particular those accessed via photo sharing sites, are expanding at a rapid rate. At the same time, internet traffic from mobile devices such as smartphones is growing at a similar speed, and hence efficient and effective techniques for managing and querying these large image repositories from mobile devices are highly sought after. In this paper, we show that this is possible using a very fast method for performing content-based image retrieval of JPEG compressed images that naturally lends itself to mobile retrieval. Our approach performs retrieval directly in the compressed domain of JPEG but, unlike previous techniques, does not require partial decompression of the encoded image data. We employ image adapted Huffman tables, which are stored in the header of JPEG files, as image descriptors and thus not only eliminate the need for decoding but require transfer of only a fraction of the image files. Image similarity is defined as the similarity between DC and AC Huffman table entries, and is shown to lead to good retrieval performance. On a benchmark database, we demonstrate retrieval accuracy similar to common compressed domain and pixel domain retrieval algorithms, yet achieve a speedup of more than 30-fold (compared to JPEG domain techniques) and more than 250-fold (compared to MPEG-7 descriptors) respectively for online image retrieval. A mobile retrieval application is shown to provide an effective way of performing image retrieval from the photo sharing website Flickr while significantly reducing bandwidth and power consumption requirements.