Cross-Modal Transfer Hashing Based on Coherent Projection

Due to the low storage and high efficiency, cross-modal hashing drew more and more attention in recent years. However, most existing methods ignore the intrinsic distribution of raw features and the inheritance relationship between raw feature space and hash space. In this paper, we propose the transfer hashing based on coherent projection for large-scale cross-modal retrieval. It preserves the inherent correlation of intrinsic distribution among raw heterogeneous features via a linear cross-modal transfer. In addition, the coherent projection is applied to cooperate with the cross-modal transfer to inherit the correlation from raw features space to hash space straightly. Furthermore, the anchor graph with linear complexity is utilized to further explore the local structure of each modality. And the semantic information is also explored by regressing the semantic labels to hash space. Finally, the succinct iterative algorithm is used for discrete optimization, which avoids continuous relaxation and reduces quantization error. Extensive experiments on two large-scale datasets show that our method has superiority in retrieval performance.