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David Chen , Stanford University, Stanford
Bernd Girod , Stanford University, Stanford
Mobile visual search (MVS) systems compare query images captured by the mobile device’s camera against a database of labeled images to recognize and augment objects seen in the device’s viewfinder. Practical MVS systems require very fast responses to provide seamless and continuous augmentation. Thus, the recognition pipeline must be extremely efficient and reliable. Congestion on a server or slow transmissions of the query data over a wireless network would severely degrade the user experience. In this paper, we report how a memory-efficient database stored entirely on a mobile device can enable on-device queries that achieve a fast response. The image signatures stored in the database must be extremely compact to fit in the small amount of memory available on the device, capable of fast comparisons across a large database, and highly discriminative to provide robust recognition for challenging queries. First, we review two methods that use compression techniques with bag-of-visual-words histograms to generate compact and discriminative signatures. They require storage of a codebook in memory and decoding of compressed signatures during a query. In contrast, we then describe two methods based on bag-of-visual-words residuals that achieve the same retrieval performance, while using a much smaller codebook and compressed domain matching.
David Chen, Bernd Girod, "Memory-efficient Image Databases for Mobile Visual Search", IEEE MultiMedia, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/MMUL.2013.46
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