NDLI: Image tag completion by low-rank factorization with dual reconstruction structure preserved

Content Provider	IEEE Xplore Digital Library
Author	Li, Xue Zhang, Yu-Jin Shen, Bin Liu, Bao-Di
Copyright Year	2014
Description	Author affiliation: Computer Science, Purdue University, West Lafayette, IN 47907, USA (Shen, Bin) \|\| Information and Control Engineering, China University of Petroleum, Qingdao, 266580, China (Liu, Bao-Di) \|\| Electronic Engineering, Tsinghua University, Beijing, 100084, China (Li, Xue; Zhang, Yu-Jin)
Abstract	A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error s-parsity: 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 structure consistency: to steer the completion of D, the local linear reconstruction 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 performance. Experiments conducted on Corel5k dataset and the newly issued Flickr30Concepts dataset demonstrate the effectiveness and efficiency of the proposed method.
Starting Page	3062
Ending Page	3066
File Size	116481
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025619
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image reconstruction Sparse matrices Matrix decomposition Noise measurement Tagging Pattern recognition Encoding LLE Tag completion Image annotation Low-rank Error sparsity
Content Type	Text
Resource Type	Article

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