IMAGE TAG COMPLETION BY LOW-RANK FACTORIZATION WITH DUAL RECONSTRUCTION STRUCTURE PRESERVED

A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error spar-sity: the incomplete initial tagging matrix D is decomposed into the complete tagging matrix A and a sparse error matrix E. However, instead of minimizing its nuclear norm, A is further factorized into a basis matrix U and a sparse coefficient matrix V, i.e. D = UV +E. This low-rank formulation encapsulating sparse coding enables our algorithm to recover latent structures from noisy initial data and avoid performing too much denoising; 2) Local reconstruction struc-ture consistency: to steer the completion ofD, the local linear recon-struction structures in feature space and tag space are obtained and preserved by U and V respectively. Such a scheme could alleviate the negative effect of distances measured by low-level features and incomplete tags. Thus, we can seek a balance between exploiting as much information and not being mislead to suboptimal perfor-mance. Experiments conducted on Corel5k dataset and the newly issued Flickr30Concepts dataset demonstrate the effectiveness and efficiency of the proposed method