Tagging image with informative and correlative tags

Authors:
Xiaoming Zhang;Heng Tao Shen;Zi Huang;Zhoujun Li
Affiliations:
School of computer, Beihang University, Beijing, China;Information technology and electrical engineering, University of Queensland, Australia;Information technology and electrical engineering, University of Queensland, Australia;School of computer, Beihang University, Beijing, China
Venue:
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Year:
2011

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Abstract

Automatic tagging can automatically label images with semantic tags to significantly facilitate multimedia search and organization. Existing tagging methods often use probabilistic or co-occurring tags, which may result in ambiguity and noise. In this paper, we propose a novel automatic tagging algorithm which tags a test image with an Informative and Correlative Tag (ICTag) set. The assigned ICTag set can provide a more precise description of the image by exploring both the information capability of individual tags and the tag-to-set correlation. Measures to effectively estimate the information capability of individual tags and the correlation between a tag and the candidate tag set are designed. To reduce the computational complexity, we also introduce a heuristic method to achieve efficient automatic tagging. The experiment results confirm the efficiency and effectiveness of our proposed algorithm.