Probabilistic semantic network-based image retrieval using MMM and relevance feedback

Authors:
Mei-Ling Shyu;Shu-Ching Chen;Min Chen;Chengcui Zhang;Chi-Min Shu
Affiliations:
Department of Electrical and Computer Engineering, University of Miami, Coral Gables, USA 33124;Distributed Multimedia Information System Laboratory, School of Computing and Information Sciences, Florida International University, Miami, USA 33199;Distributed Multimedia Information System Laboratory, School of Computing and Information Sciences, Florida International University, Miami, USA 33199;Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, USA 35294;Department of Environmental and Safety Engineering, National Yunlin University of Science and Technology, Yunlin, Republic of China
Venue:
Multimedia Tools and Applications
Year:
2006

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Abstract

The performance of content-based image retrieval (CBIR) systems is largely limited by the gap between the low-level features and high-level semantic concepts. In this paper, a probabilistic semantic network-based image retrieval framework using relevance feedback is proposed to bridge this gap, which not only takes into consideration the low-level image content features, but also learns high-level concepts from a set of training data, such as access frequencies and access patterns of the images. One of the distinct properties of our framework is that it exploits the structured description of visual contents as well as the relative affinity measurements among the images. Consequently, it provides the capability to bridge the gap between the low-level features and high-level concepts. Moreover, such high-level concepts can be learned off-line, and can be utilized and refined based on the user's specific interest during the on-line retrieval process. Our experimental results demonstrate that the proposed framework can effectively assist in retrieving more accurate results for user queries.