Low-rank image tag completion with dual reconstruction structure preserved

User provided tags, albeit play an essential role in image annotation, may inhibit accurate annotation as well since they are potentially incomplete. To address this problem, a novel tag completion method is proposed in this paper. In order to exploit as much information, the proposed method is designed with the following features: (1) Low-rank and error sparsity: the initial tag matrix D is decomposed into the complete tag matrix A and a sparse error matrix E, where A is further factorized into a basis matrix U and a sparse coefficient matrix V, i.e.  , D=UV+ED=UV+E. With K⪡MK⪡M, information sharing between related tags and similar samples can be achieved via subspace construction. (2) Local reconstruction structure consistency: the local linear reconstruction structures obtained in the original feature and tag spaces are preserved in both the low-dimensional feature subspace and tag subspace. (3) Promote basis diversity: the pair-wise dot products between the columns of U are minimized, in order to obtain more representative basis vectors. Experiments conducted on Corel5K dataset and the newly issued Flickr30Concepts dataset demonstrate the effectiveness and efficiency of the proposed method.