Automatic image annotation based on topic-based smoothing

Authors:
Xiangdong Zhou;Jianye Ye;Lian Chen;Liang Zhang;Baile Shi
Affiliations:
Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

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Abstract

Automatic image annotation has attracted much attention recently, due to its wide applicability (such as image retrieval by semantics). Most of the known statistical model-based annotation methods learn the joint distribution of the keywords and the image blobs decomposed by segmentation or gride approaches. The effects of these methods suffer from the sparseness of the image blobs. As a result, the estimated joint distribution is need to be “smoothed”. In this paper, we present a topic-based smoothing method to overcome the sparseness problems, and integrated with a general image annotation model. Experimental results on 5,000 images demonstrate that our method can achieves significant improvement in annotation effectiveness over an existing method.