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Image Retrieval Using Transaction-Based and SVM-Based Learning in Relevance Feedback Sessions

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Image Analysis and Recognition (ICIAR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4633))

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Abstract

This paper introduces a composite relevance feedback approach for image retrieval using transaction-based and SVM-based learning. A transaction repository is dynamically constructed by applying these two learning techniques on positive and negative session-term feedback. This repository semantically relates each database image to the query images having been used to date. The query semantic feature vector can then be computed using the current feedback and the semantic values in the repository. The correlation measures the semantic similarity between the query image and each database image. Furthermore, the SVM is applied on the session-term feedback to learn the hyperplane for measuring the visual similarity between the query image and each database image. These two similarity measures are normalized and combined to return the retrieved images. Our extensive experimental results show that the proposed approach offers average retrieval precision as high as 88.59% after three iterations. Comprehensive comparisons with peer systems reveal that our system yields the highest retrieval accuracy after two iterations.

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Authors and Affiliations

  1. Computer Science Department, Utah State University, Logan, UT 84322-4205,  

    Xiaojun Qi & Ran Chang

Authors
  1. Xiaojun Qi
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  2. Ran Chang
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Editor information

Mohamed Kamel Aurélio Campilho

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© 2007 Springer-Verlag Berlin Heidelberg

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Qi, X., Chang, R. (2007). Image Retrieval Using Transaction-Based and SVM-Based Learning in Relevance Feedback Sessions. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_57

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  • DOI: https://doi.org/10.1007/978-3-540-74260-9_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74258-6

  • Online ISBN: 978-3-540-74260-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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