A regularized optimization framework for tag completion and image retrieval

Abstract With the fast expansion of social image sharing websites, the tag-based image retrieval (TBIR) becomes important and prevalent for Internet users to search the social images. However, some user-provided tags of social images are too incomplete and ambiguous to facilitate the social image retrieval. In this paper, we propose a regularized optimization framework to complete the missing tags for social images ( tag completion ). Within the regularized optimization framework, the non-negative matrix factorization (NMF) and the holistic visual diversity minimization are used jointly to make the tag-image matrix completed as the relationships of images and tags are represented to a tag-image matrix. The non-negative matrix factorization casts the tag-image matrix into a latent low-rank space and utilizes the semantic relevance of tags to partially complete the insufficient tags. To take the visual content of images into account, the other objective term representing the holistic visual diversity is appended with the NMF to leverage the content-similar images. Moreover, to ensure the proper corrections and sparseness of tag-image matrix, two regularized factors are also included into the optimization framework. Through conducting the experiments on the benchmark image set with the adequate ground truth, we verify the effectiveness of our proposed approach.