Automatic image annotation using semantic relevance

Authors:
Peng Zhao;Yijuan Lu;WenBin Wang;WeiWei Zhu
Affiliations:
Anhui University, Hefei, P.R. China;Texas State University, San Marcos, TX;Anhui University, Hefei, P.R. China;Anhui University, Hefei, P.R. China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

Due to the semantic gap between low-level visual feature and high-level semantic concept, image annotation plays an important role in image retrieval. In this paper, an automatic image annotation approach using semantic relevance is proposed. It constructs an improved probabilistic model to characterize different regions' contributions to the semantics more accurately based on the spatial, visual and contextual information of the region. And it also helps expand the coverage of the semantic concept with semantic relevance information. The performance of the proposed approach has been evaluated on the standard Corel dataset. The experimental results have demonstrated its potential and effectiveness.