Unsupervised Multi-modal Hashing for Cross-modal retrieval
This addresses efficient cross-modal retrieval for big data applications, representing an incremental improvement over existing hashing methods.
The paper tackles cross-modal retrieval by proposing an unsupervised multi-modal hashing method that preserves manifold structure while exploring semantic correlations in textual space and geometric structure in visual space, achieving superior performance over state-of-the-art methods on three public datasets.
With the advantage of low storage cost and high efficiency, hashing learning has received much attention in the domain of Big Data. In this paper, we propose a novel unsupervised hashing learning method to cope with this open problem to directly preserve the manifold structure by hashing. To address this problem, both the semantic correlation in textual space and the locally geometric structure in the visual space are explored simultaneously in our framework. Besides, the `2;1-norm constraint is imposed on the projection matrices to learn the discriminative hash function for each modality. Extensive experiments are performed to evaluate the proposed method on the three publicly available datasets and the experimental results show that our method can achieve superior performance over the state-of-the-art methods.