MIHash: Online hashing with mutual information
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Published version
Date
2017-10-22
DOI
Authors
Fatih, Cakir
He, Kun
Bargal, Sarah Adel
Sclaroff, Stanley
Version
OA Version
Citation
Cakir Fatih, Kun He, Sarah Adel Bargal, Stanley Sclaroff. 2017. "MIHash: Online hashing with mutual information." International Conference on Computer Vision
Abstract
Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data. In this paper, we first address a key challenge for online hashing: the binary codes for indexed data must be recomputed to keep pace with updates to the hash functions. We propose an efficient quality measure for hash functions, based on an information-theoretic quantity, mutual information, and use it successfully as a criterion to eliminate unnecessary hash table updates. Next, we also show how to optimize the mutual information objective using stochastic gradient descent. We thus develop a novel hashing method, MIHash, that can be used in both online and batch settings. Experiments on image retrieval benchmarks (including a 2.5M image dataset) confirm the effectiveness of our formulation, both in reducing hash table recomputations and in learning high-quality hash functions.
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This ICCV 2017 paper is the Open Access version, provided by the Computer Vision Foundation. Except for the watermark it is identical to the version available on IEEE Xplore.
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