Multi-Kernel Supervised Hashing with Graph Regularization for Cross-Modal Retrieval

Hashing based approximate nearest neighbor search has received considerable attention due to the demand of fast query for big multimedia data. Cross-modal hashing focuses on retrieval tasks across different modalities, which is more useful in practical applications. In this paper, we propose a novel two-stage cross-modal hashing method, referred to as Multi-Kernel Supervised Hashing with Graph Regularization (MKSRH). To better capture the essential attribute of original data, MKSRH first maps original data to a kernel space constructed by a linear combination of multiple kernel functions. Then the preliminary hash functions are learned using the Adaboost framework in the kernel space. To produce more accurate hash codes, the obtained hash function are then refined using a graph regularization based strategy. Experimental results on two canonical datasets show that MKSRH significantly outperforms than some typical cross-modal hashing methods, demonstrating the effectiveness and the superiority of the proposed approach.