Probabilistic Image Tagging with Tags Expanded By Text-Based Search

Automatic image tagging automatically assigns image with semantic keywords called tags, which significantly facilitates image search and organization. Most of present image tagging approaches assign the query image with the tags derived from the visually similar images in the training dataset only. However, their scalabilities and performances are constrained by the limitation of using the training method and the fixed size tag vocabulary. In this paper, we proposed a search based probabilistic image tagging algorithm (CTSTag), in which the initially assigned tags are mined from the content-based search result and expanded from the text-based search results. Experiments on NUS-WIDE dataset show not only the performance of the proposed algorithm but also the advantage of image retrieval using the tagging result.

[1]  Anthony K. H. Tung,et al.  Multiple feature fusion for social media applications , 2010, SIGMOD Conference.

[2]  Stephen E. Robertson,et al.  A probabilistic model of information retrieval: development and comparative experiments - Part 1 , 2000, Inf. Process. Manag..

[3]  W. Bruce Croft.  Advances in Information Retrieval , 2000, The Information Retrieval Series.

[4]  Marcel Worring,et al.  Learning Social Tag Relevance by Neighbor Voting , 2009, IEEE Transactions on Multimedia.

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

[6]  Dong Liu,et al.  Tag ranking , 2009, WWW '09.

[7]  Wei-Ying Ma,et al.  IGroup: web image search results clustering , 2006, MM '06.

[8]  James Ze Wang,et al.  PARAgrab: a comprehensive architecture for web image management and multimodal querying , 2006, VLDB.

[9]  Christos Faloutsos,et al.  Fast Random Walk with Restart and Its Applications , 2006, Sixth International Conference on Data Mining (ICDM'06).

[10]  Nenghai Yu,et al.  Distance metric learning from uncertain side information with application to automated photo tagging , 2009, ACM Multimedia.

[11]  Marcel Worring,et al.  Learning tag relevance by neighbor voting for social image retrieval , 2008, MIR '08.

[12]  Changhu Wang,et al.  Image annotation refinement using random walk with restarts , 2006, MM '06.

[13]  Bingjun Zhang,et al.  Comprehensive query-dependent fusion using regression-on-folksonomies: a case study of multimodal music search , 2009, ACM Multimedia.

[14]  Xian-Sheng Hua,et al.  Collaborative learning for image and video annotation , 2008, MIR '08.

[15]  Wei-Ying Ma,et al.  Annotating Images by Mining Image Search Results , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Wei-Ying Ma,et al.  IGroup: a web image search engine with semantic clustering of search results , 2006, MM '06.

[17]  Qi Zhang,et al.  Automatic image annotation by an iterative approach: incorporating keyword correlations and region matching , 2007, CIVR '07.

[18]  Changhu Wang,et al.  Scalable search-based image annotation , 2008, Multimedia Systems.

[19]  James Ze Wang,et al.  Real-Time Computerized Annotation of Pictures , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Mark Sanderson,et al.  Automatic video tagging using content redundancy , 2009, SIGIR.

[21]  W. Bruce Croft.  Language models for information retrieval , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[22]  David A. Forsyth,et al.  Matching Words and Pictures , 2003, J. Mach. Learn. Res..