Searching with expectations

Harsimrat Sandhawalia 1 Hervé Jégou 2
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Handling large amounts of data, such as large image databases, requires the use of approximate nearest neighbor search techniques. Recently, Hamming embedding methods such as spectral hashing have addressed the problem of obtaining compact binary codes optimizing the trade-off between the memory usage and the probability of retrieving the true nearest neighbors. In this paper, we formulate the problem of generating compact signatures as a rate-distortion problem. In the spirit of source coding algorithms, we aim at minimizing the reconstruction error on the squared distances with a constraint on the memory usage. The vectors are ranked based on the distance estimates to the query vector. Experiments on image descriptors show a significant improvement over spectral hashing.
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Harsimrat Sandhawalia, Hervé Jégou. Searching with expectations. ICASSP 2010 - IEEE International Conference on Acoustics Speech and Signal Processing, IEEE, Mar 2010, Dallas, United States. pp.1242-1245, ⟨10.1109/ICASSP.2010.5495403⟩. ⟨inria-00548629⟩

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