Learning Hash Functions for Cross-View Similarity Search
Shaishav Kumar and Raghavendra Udupa
Many applications in Multilingual and Multimodal Information Access involve searching large databases of high dimensional data objects with multiple (conditionally independent) views. In this work we consider the problem of learning hash functions for similarity search across the views for such applications. We propose a principled method for learning a hash function for each view given a set of multiview training data objects. The hash functions map similar objects to similar codes across the views thus enabling cross-view similarity search. Further, our approach results in, as a special case, a new hashing technique for data objects with single view. We present results from an extensive empirical study of the proposed approach which demonstrate its effectiveness on both synthetic and real data objects.