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

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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.

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Acknowledgments

For Mei-Ling Shyu, this research was supported in part by NSF ITR (Medium) IIS-0325260. For Shu-Ching Chen, this research was supported in part by NSF HRD-0317692 and NSF EIA-0220562. For Chengcui Zhang, this research was supported in part by UAB Faculty Development Award, SBE-0245090 and the UAB ADVANCE program of the Office for the Advancement of Women in Science and Engineering.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, 33124, USA

    Mei-Ling Shyu

  2. Distributed Multimedia Information System Laboratory, School of Computing and Information Sciences, Florida International University, Miami, FL, 33199, USA

    Shu-Ching Chen & Min Chen

  3. Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

    Chengcui Zhang

  4. Department of Environmental and Safety Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan, Republic of China

    Chi-Min Shu

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  1. Mei-Ling Shyu
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  2. Shu-Ching Chen
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  3. Min Chen
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  4. Chengcui Zhang
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  5. Chi-Min Shu
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Correspondence to Mei-Ling Shyu.

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Shyu, ML., Chen, SC., Chen, M. et al. Probabilistic semantic network-based image retrieval using MMM and relevance feedback. Multimed Tools Appl 30, 131–147 (2006). https://doi.org/10.1007/s11042-006-0023-5

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