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Hash based nearest neighbor search has become attractive in manyapplications. However, the quantization in hashing usually degenerates thediscriminative power when using Hamming distance ranking. Besides, forlarge-scale visual search, existing hashing methods cannot directly support theefficient search over the data with multiple sources, and while the literaturehas shown that adaptively incorporating complementary information from diversesources or views can significantly boost the search performance. To address theproblems, this paper proposes a novel and generic approach to building multiplehash tables with multiple views and generating fine-grained ranking results atbitwise and tablewise levels. For each hash table, a query-adaptive bitwiseweighting is introduced to alleviate the quantization loss by simultaneouslyexploiting the quality of hash functions and their complement for nearestneighbor search. From the tablewise aspect, multiple hash tables are built fordifferent data views as a joint index, over which a query-specific rank fusionis proposed to rerank all results from the bitwise ranking by diffusing in agraph. Comprehensive experiments on image search over three well-knownbenchmarks show that the proposed method achieves up to 17.11% and 20.28%performance gains on single and multiple table search over state-of-the-artmethods.

Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search