Semi Paired Discrete Hashing: Learning Latent Hash Codes for Semi Paired Cross View Retrieval
Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large scale cross view retrieval tasks. Most cross view hashing methods are developed by assuming that data from different views are well paired, e.g. text image pairs. In real world applications, however, this fully paired multiview setting may not be practical. The more practical yet challenging semi paired cross view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi paired cross view retrieval, dubbed semi paired discrete hashing (SPDH) In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi paired data in the latent subspace, we construct the cross view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit by bit manner with each bit generated with a closed form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully paired and semi paired settings and the results demonstrate the superiority of SPDH over several other state of the art methods in term of both accuracy and scalability.
IEEE Transactions on Cybernetics