Tag Refinement for User-Contributed Images via Graph Learning and Nonnegative Tensor Factorization

Social image tagging systems mostly suffer from poor performance for image retrieval due to the noisy and incomplete correspondences between user-contributed images and their associated tags. In this letter, we aim to refine tag allocations in the social tagging data provided by these systems. In particular, we propose to harness the tagged and untagged data with a two-stage strategy according to different types of data relations, i.e. item similarity defined by prior knowledge and item co-occurrence learned from data statistics. To solve the sparsity problem, we first introduce a new graph learning (GL) method for enriching the tagging data according to item similarities. Then, we develop a method of nonnegative tensor factorization (NTF) for learning more coherent ternary relations among users, images and tags coupled by the manifold constraints learned from item co-occurrences. Experimental results with the tagging data from the NUS-WIDE dataset have been reported to validate the effectiveness of the proposed method.