A Locality Sensitive Low-Rank Model for Image Tag Completion.Many visual applications have benefited from the outburst of web images, yet the imprecise and incomplete tags arbitrarily provided by users, as the thorn of the rose, may hamper the performance of retrieval or indexing systems relying on such data.In this paper, we propose a novel locality sensitive low-rank model for image tag completion, which approximates the global nonlinear model with a collection of local linear models.Extensive empirical evaluations conducted on three datasets demonstrate the effectiveness and efficiency of the proposed method, where our method outperforms pervious ones by a large margin.

 

The advent of the big data era has witnessed an explosive growth of the visual data, which has spawned many visual applications to organize, analyze, and retrieve these images. 

 

This will pose threats to the retrieval or indexing of these images, causing them difficult to be accessed by users. Unfortunately, missing label is inevitable in the manual labeling phase, since it is infeasible for users to label every related word 

 

Therefore, image tag completion or refinement has emerged as a hot issue in the multimedia community.