Subspace-based self-weighted multiview fusion for instance retrieval

Most existing subspace-based multiview learning algorithms treat multiview features equally in the process of subspace reconstruction without distinguishing the discrepant contributions from different views. This limitation adversely affects subspace embedding, particularly when significant variances in view-specific feature characteristics are present. In this paper, a unified subspace-based self-weighted multiview learning framework (SSMVL) is proposed for instance retrieval. In contrast to the previous methods in which the variances in multiview features are ignored, the self-weighted learning mechanism is integrated into the multiview subspace learning framework such that the weights of different views are adaptively learned instead of empirically assigned. In addition, the proposed SSMVL approach falls into the unsupervised learning category and is thus independent of massive amounts of labeled data resulting from labor-intensive annotation. In this study, the extension of SSMVL to the Hamming subspace learning paradigm is also explored for efficient retrieval. Experiments on five public benchmarks reveal that the self-weighted learning strategy plays a beneficial role in multiview fusion, and our method achieves superior performance in comparison to state-of-the-art methods.