Modality-specific matrix factorization hashing for cross-modal retrieval

Cross-modal retrieval has been attracted attentively in the past years. Recently, the collective matrix factorization was proposed to learn the common representations for cross-modal retrieval based on assumption that the pairwise data from different modalities should have the same common semantic representations. However, this unified common representation could inherently sacrifice the modality-specific representations for each modality because the distributions and representations of different modalities are inconsistent. To mitigate this problem, in this paper, we propose Modality-specific Matrix Factorization Hashing (MsMFH) via alignment, which learns the modality-specific semantic representation for each modality and then aligns the representations via the correlation information. Specifically, we factorize the original feature representations into individual latent semantic representations, and then align the distributions of individual latent semantic representations via an orthogonal transformation. Then, we embed the class label into the hash codes learning via latent semantic space, and obtain hash codes directly by an efficient optimization with a closed solution. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms to many existing cross-modal hashing methods up to 3% in term of mean average precision (mAP).