Supervised matrix factorization hashing for cross-modal retrieval

Tang, Jun, Wang, Ke and Shao, Ling (2016) Supervised matrix factorization hashing for cross-modal retrieval. IEEE Transactions on Image Processing, 25 (7). pp. 3157-3166. ISSN 1057-7149

PDF (Accepted manuscript) - Submitted Version
Download (1MB) | Preview


The target of cross-modal hashing is to embed heterogeneous multimedia data into a common low-dimensional Hamming space, which plays a pivotal part in multimedia retrieval due to the emergence of big multimodal data. Recently, matrix factorization has achieved great success in cross-modal hashing. However, how to effectively use label information and local geometric structure is still a challenging problem for these approaches. To address this issue, we propose a cross-modal hashing method based on collective matrix factorization, which considers both the label consistency across different modalities and the local geometric consistency in each modality. These two elements are formulated as a graph Laplacian term in the objective function, leading to a substantial improvement on the discriminative power of latent semantic features obtained by collective matrix factorization. Moreover, the proposed method learns unified hash codes for different modalities of an instance to facilitate cross-modal search, and the objective function is solved using an iterative strategy. The experimental results on two benchmark data sets show the effectiveness of the proposed method and its superiority over state-of-the-art cross-modal hashing methods.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 09 Mar 2017 01:41
Last Modified: 22 Jul 2020 01:21
DOI: 10.1109/TIP.2016.2564638

Actions (login required)

View Item View Item