MIHash: Online Hashing with Mutual Information

Learning-based adaptive hashing methods are widely used for nearest neighbor retrieval. Recently, online hashing methods have demonstrated a good performance-complexity tradeoff by learning hash functions from streaming data. In this paper, we aim to advance the state-of-the-art for online hashing. We first address a key challenge that has often been ignored: 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 show that mutual information can also be used as an objective in learning hash functions, using gradient-based optimization. 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.