Dong Liu
Xian-Sheng Hua
ABSTRACT
The tags on social media websites such as Flickr are frequently imprecise and incomplete, thus there is still a gap between these tags and the actual content of the images. This paper proposes a social image ``retagging'' scheme that aims at assigning images with better content descriptors. The refining process is formulated as an optimization framework based on the consistency between ``visual similarity'' and ``semantic similarity'' in social images. An effective iterative bound optimization algorithm is applied to learn the optimal tag assignment. In addition, as many tags are intrinsically not closely-related to the visual content of the images, we employ a knowledge-based method to differentiate visual content related from unrelated tags and then constrain the tagging vocabulary of our automatic algorithm within the content related tags. Experimental results on a Flickr image collection demonstrate the effectiveness of this approach.
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Digital Library
- C. Fellbaum. Wordnet: An Electronic Lexical Database. Bradford Books, 1998.Google ScholarCross Ref
- C. Wang, F. Jing, L. Zhang and H. J. Zhang. Content-Based Image Annotation Refinement. In Proceeding of IEEE International Conference on Computer Vision and Pattern Recognition, 2007.Google ScholarCross Ref
- D. Lin. Using Syntatic Dependency as a Local Context to Resolve Word Sense Ambiguity. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, 1997. Google ScholarDigital Library
- Y. Liu, R. Jin and L. Yang. Semi-supervised Multi-label Learning by Constrained Non-Negative Matrix Factorization. In Proceedings of the 21st National Conference on Artificial Intelligence, 2006. Google ScholarDigital Library
- D. Liu, M. Wang, L. Yang, X.-S. Hua and H. J. Zhang. Tag Quality Improvement for Social Images. In International Conference on Multimedia & Expo, 2009. Google ScholarDigital Library
Index Terms
- Retagging social images based on visual and semantic consistency
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