MIHash: Online Hashing with Mutual Information
This work addresses a key problem in online hashing for nearest neighbor retrieval, offering an incremental improvement by optimizing hash function updates using mutual information.
The paper tackled the challenge of recomputing binary codes for indexed data in online hashing by proposing an efficient quality measure based on mutual information to eliminate unnecessary hash table updates, resulting in reduced recomputations and high-quality hash functions validated on image retrieval benchmarks including a 2.5M image dataset.
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.