Image Tag Completion via Image-Specific and Tag-Specific Linear Sparse Reconstructions

Though widely utilized for facilitating image management, user-provided image tags are usually incomplete and insufficient to describe the whole semantic content of corresponding images, resulting in performance degradations in tag-dependent applications and thus necessitating effective tag completion methods. In this paper, we propose a novel scheme denoted as LSR for automatic image tag completion via image-specific and tag-specific Linear Sparse Reconstructions. Given an incomplete initial tagging matrix with each row representing an image and each column representing a tag, LSR optimally reconstructs each image (i.e. row) and each tag (i.e. column) with remaining ones under constraints of sparsity, considering image-image similarity, image-tag association and tag-tag concurrence. Then both image-specific and tag-specific reconstruction values are normalized and merged for selecting missing related tags. Extensive experiments conducted on both benchmark dataset and web images well demonstrate the effectiveness of the proposed LSR.

[1]  R. Manmatha,et al.  Multiple Bernoulli relevance models for image and video annotation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[2]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[3]  Dong Liu,et al.  Image Retagging Using Collaborative Tag Propagation , 2011, IEEE Transactions on Multimedia.

[4]  Shuicheng Yan,et al.  Image tag refinement towards low-rank, content-tag prior and error sparsity , 2010, ACM Multimedia.

[5]  Mor Naaman,et al.  Why we tag: motivations for annotation in mobile and online media , 2007, CHI.

[6]  Wesley De Neve,et al.  MAP-based image tag recommendation using a visual folksonomy , 2010, Pattern Recognit. Lett..

[7]  Wesley De Neve,et al.  Image tag refinement along the ‘what’ dimension using tag categorization and neighbor voting , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[8]  Hai Jin,et al.  Image label completion by pursuing contextual decomposability , 2012, TOMCCAP.

[9]  Lei Wu,et al.  Tag Completion for Image Retrieval , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Yueting Zhuang,et al.  Tag Clustering and Refinement on Semantic Unity Graph , 2011, 2011 IEEE 11th International Conference on Data Mining.

[11]  Dong Liu,et al.  Image retagging , 2010, ACM Multimedia.

[12]  Vladimir Pavlovic,et al.  A New Baseline for Image Annotation , 2008, ECCV.

[13]  Nenghai Yu,et al.  Learning to tag , 2009, WWW '09.

[14]  Cordelia Schmid,et al.  TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  Changhu Wang,et al.  Content-Based Image Annotation Refinement , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Yang Yu,et al.  Automatic image annotation using group sparsity , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.