Robust image annotation refinement via graph-based learning

Authors:
Xiaohong Hu;Xu Qian;Lei Xi;Xinming Ma
Affiliations:
School of Information and Management Science, Henan Agricultural University, Zhengzhou, China and School of Mechanical Electronic and Information Engineering, China University of Mining and Techno ...;School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China;School of Information and Management Science, Henan Agricultural University, Zhengzhou, China;School of Information and Management Science, Henan Agricultural University, Zhengzhou, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

Image annotation has been an active research topic in recent years. However, the state of art image annotation methods are often unsatisfactory, in this paper, we presented a novel image annotation refinement to improve the performance of automatic image annotation. Firstly, the initial pair-wise similarities of words is computed based on the co-occurrence of training sets, Then the topic relation is mined by generating the topic bag. Finally, the candidate annotations are re-ranked by embedding the refined word relation. The experiments over Corel images have shown that embedding topic relation is beneficial in image annotation.