Cross-view Retrieval via Probability-based Semantics-Preserving Hashing

For efficiently retrieving nearest neighbours from large-scale multi-view data, recently hashing methods are widely investigated, which can substantially improve query speeds. In this paper, we propose an effective probability-based Semantics-Preserving Hashing method to tackle the problem of cross-view retrieval, termed SePH. Considering the semantic consistency between views, SePH generates one unified hash code for all observed views of any instance. For training, SePH firstly transforms the given semantic affinities of training data into a probability distribution, and aims to approximate it with another one in Hamming space, via minimizing their Kullback-Leibler divergence. Specifically, the latter probability distribution is derived from all pair-wise Hamming distances between to be-learnt hash codes of the training data. Then with learnt hash codes, any kind of predictive models like linear ridge regression, logistic regression or kernel logistic regression, can be learnt as hash functions in each view for projecting the corresponding view-specific features into hash codes. As for out of-sample extension, given any unseen instance, the learnt hash functions in its observed views can predict view-specific hash codes. Then by deriving or estimating the corresponding output probabilities w.r.t the predicted view-specific hash codes, a novel probabilistic approach is further proposed to utilize them for determining a unified hash code. To evaluate the proposed SePH, we conduct extensive experiments on diverse benchmark datasets,and the experimental results demonstrate that SePH is reasonable and effective.