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Reducing Manual Feedback in a Distributed CBIR System

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Advances in Multimedia Information Processing - PCM 2009 (PCM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5879))

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Abstract

This paper presents a study of user interactivity for content-based image retrieval. A single-click relevance feedback technique is examined in this paper, and its retrieval precision is evaluated against conventional multi-click relevance feedback. Given that single-click relevance feedback speeds up manual selection process, self-organizing tree map is used to cluster the image database, to reduce the computational load in the proposed system. A retrieval precision versus number of clicks comparison is examined in this paper to compare the relative retrieval precisions of both techniques under same levels of manual interactions.

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References

  1. Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1349–1380 (2000)

    Article  Google Scholar 

  2. Rui, Y., Huang, T., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transactions on circuits and systems for video technology 8, 644–655 (1998)

    Article  Google Scholar 

  3. Bosque, J., Robles, O., Pastor, L., Rodriguez, A.: Parallel CBIR implementations with load balancing algorithms. Journal of Parallel and Distributed Computing 66, 1062–1075 (2006)

    Article  MATH  Google Scholar 

  4. Lee, I., Guan, L.: Content-Based Image Retrieval with Automated Relevance Feedback over Distributed Peer-to-Peer Network. In: Proc. of IEEE International Symposium on Circuits and Systems (ISCAS), Vancouver, Canada, May 2004, pp. 5–8 (2004)

    Google Scholar 

  5. Chen, Y., Wang, J., Krovetz, R.: CLUE: Cluster-based retrieval of images by unsupervised learning. IEEE Transactions on Image Processing 14, 1187–1201 (2005)

    Article  Google Scholar 

  6. Muneesawang, P., Guan, L.: An interactive approach for CBIR using a network of radial basis functions. IEEE Transactions on multimedia 6, 703–716 (2004)

    Article  Google Scholar 

  7. Lee, I., Guan, L.: Semi-Automated Relevance Feedback for Distributed Content Based Image Retrieval. In: Proc. of IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan, June 2004, pp. 1871–1874 (2004)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Lee, I., Muneesawang, P. (2009). Reducing Manual Feedback in a Distributed CBIR System. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_86

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  • DOI: https://doi.org/10.1007/978-3-642-10467-1_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10466-4

  • Online ISBN: 978-3-642-10467-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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