Discrete matrix factorization cross-modal hashing with multi-similarity consistency

Abstract

AbstractRecently, matrix factorization-based hashing has gained wide attention because of its strong subspace learning ability and high search efficiency. However, some problems need to be further addressed. First, uniform hash codes can be generated by collective matrix factorization, but they often cause serious loss, degrading the quality of hash codes. Second, most of them preserve the absolute similarity simply in hash codes, failing to capture the inherent semantic affinity among training data. To overcome these obstacles, we propose a Discrete Multi-similarity Consistent Matrix Factorization Hashing (DMCMFH). Specifically, an individual subspace is first learned by matrix factorization and multi-similarity consistency for each modality. Then, the subspaces are aligned by a shared semantic space to generate homogenous hash codes. Finally, an iterative-based discrete optimization scheme is presented to reduce the quantization loss. We conduct quantitative experiments on three datasets, MSCOCO, Mirflickr25K and NUS-WIDE. Compared with supervised baseline methods, DMCMFH achieves increases of $$0.22\%$$ 0.22 % , $$3.00\%$$ 3.00 % and $$0.79\%$$ 0.79 % on the image-query-text tasks for three datasets respectively, and achieves increases of $$0.21\%$$ 0.21 % , $$1.62\%$$ 1.62 % and $$0.50\%$$ 0.50 % on the text-query-image tasks for three datasets respectively.