Correlation learning method based on image internal semantic model for CBIR

Authors:
Lijuan Duan;Guojun Mao;Wen Gao
Affiliations:
The College of Computer Science, Beijing University of Technology, Beijing, China;The College of Computer Science, Beijing University of Technology, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Year:
2004

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Abstract

Semantic-based image retrieval is the desired target of Content-based image retrieval (CBIR). In this paper, we proposed a new method to extract semantic information for CBIR using the relevance feedback results. Firstly it is assumed that positive and negative examples in relevant feedback are containing semantic content added by users. Then image internal semantic model (IISM) is proposed to represent comprehensive pair-wise correlation information for images through analyzing the feedback results. Finally, correlation learning method is proposed to represent the images' pair-wise relationship based on statistical value of access path, access frequency, similarity factor and correlation factor. Experimental results on Corel datasets show the effectiveness of the proposed model and method.