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A novel long-term learning algorithm for relevance feedback in content-based image retrieval

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Abstract

Relevance feedback has proven to be a powerful tool to bridge the semantic gap between low-level features and high-level human concepts in content-based image retrieval (CBIR). However, traditional short-term relevance feedback technologies are confined to using the current feedback record only. Log-based long-term learning captures the semantic relationships among images in a database by analyzing the historical relevance information to boost the retrieval performance effectively. In this paper, we propose an expanded-judging model to analyze the historical log data’s semantic information and to expand the feedback sample set from both positive and negative relevant information. The index table is used to facilitate the log analysis. The expanded-judging model is applied in image retrieval by combining with short-term relevance feedback algorithms. Experiments were carried out to evaluate the proposed algorithm based on the Corel image database. The promising experimental results validate the effectiveness of our proposed expanded-judging model.

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Acknowledgements

The authors would like to thank the reviews for insight comments that helped to improve the paper.

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Authors and Affiliations

  1. School of Information Engineering, Communication University of China, Beijing, China

    Hui Lu & Lifang Yang

  2. School of Computer Science, Communication University of China, Beijing, China

    Xianglin Huang & Min Liu

Authors
  1. Hui Lu
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  2. Xianglin Huang
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  3. Lifang Yang
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  4. Min Liu
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Corresponding author

Correspondence to Hui Lu.

Additional information

This paper was recommended by MINES 2010, and was supported in part by the Doctoral Fund of Ministry of Education of China (20090018110001), the program for New Century Excellent Talents in University (NCET-07-0768), and 211 Project of China.

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Lu, H., Huang, X., Yang, L. et al. A novel long-term learning algorithm for relevance feedback in content-based image retrieval. Telecommun Syst 54, 265–275 (2013). https://doi.org/10.1007/s11235-013-9732-z

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