Tag quality improvement for social images

  • Authors:
  • Dong Liu;Meng Wang;Linjun Yang;Xian-Sheng Hua;HongJiang Zhang

  • Affiliations:
  • School of Computer Sci. & Tec., Harbin Institute of Technology;Microsoft Research Asia;Microsoft Research Asia;Microsoft Research Asia;Microsoft Advanced Technology Center

  • Venue:
  • ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Online social media sharing web sites like Flickr allow users to manually annotate images with tags, which can facilitate image search and organization. However, the tags provided by users are often imprecise and incomplete, which severely limits the application of tags to image search and browse. In this paper, we propose a scheme to improve poorly annotated tags associated with social images. Two properties are exploited and integrated in an unified optimization framework: (1) consistency between visual and semantic similarities, where the semantic similarity is estimated using tags; (2) compatibility of tags before and after improvement, since the initial user provided tags carry valuable information. An iterative bound optimization method is derived to solve the optimization problem. Experimental results on Flickr dataset show that the proposed method can significantly improve the quality of tags.