Beyond "Near-Duplicates": Learning Hash Codes for Efficient Similar-Image Retrieval

Finding similar images in a large database is an important, but often computationally expensive, task. In this paper, we present a two-tier similar-image retrieval system with the efficiency characteristics found in simpler systems designed to recognize nearduplicates. We compare the efficiency of lookups based on random projections and learned hashes to 100-times-more-frequent exemplar sampling. Both approaches significantly improve on the results from exemplar sampling, despite having significantly lower computational costs. Learned-hash keys provide the best result, in terms of both recall and efficiency.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Shumeet Baluja, Michele Covell
• Keywords: 100-times-more-frequent Exemplar Sampling, Computer Vision, Exemplar Sampling, ICPR 2010, Two-tier Similar-image Retrieval