Telecommunication Systems

, Volume 54, Issue 3, pp 265–275 | Cite as

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

Article
First Online:

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.

Keywords

Content-based image retrieval Relevance feedback Long-term learning Log data Semantic gap 
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Notes

Acknowledgements

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

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Information EngineeringCommunication University of ChinaBeijingChina
  2. 2.School of Computer ScienceCommunication University of ChinaBeijingChina

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