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Relevance Feedback Models for Content-Based Image Retrieval

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Multimedia Analysis, Processing and Communications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 346))

Abstract

We investigate models for content-based image retrieval with relevance feedback, in particular focusing on the exploration-exploitation dilemma. We propose quantitative models for the user behavior and investigate implications of these models. Three search algorithms for efficient searches based on the user models are proposed and evaluated. In the first model a user queries a database for the most (or a sufficiently) relevant image. The user gives feedback to the system by selecting the most relevant image from a number of images presented by the system. In the second model we consider a filtering task where relevant images should be extracted from a database and presented to the user. The feedback of the user is a binary classification of each presented image as relevant or irrelevant. While these models are related, they differ significantly in the kind of feedback provided by the user. This requires very different mechanisms to trade off exploration (finding out what the user wants) and exploitation (serving images which the system believes relevant for the user).

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References

  1. Auer, P.: Using Confidence Bounds for Exploitation-Exploration Trade-offs. Journal of Machine Learning Research 3, 397–422 (2002)

    Article  MathSciNet  Google Scholar 

  2. Leung, A.P., Auer, P.: An Efficient Search Algorithm for Content-Based Image Retrieval with User Feedback. In: 1st Int. Workshop on Video Mining (VM 2008) in association with IEEE International Conference on Data Mining, ICDM 2008 (2008)

    Google Scholar 

  3. Chang, E., Tong, S., Goh, K., Chang, C.: Support Vector Machine Concept-Dependent Active Learning for Image Retrieval. IEEE Transactions on Multimedia (2005)

    Google Scholar 

  4. Chen, Y., Zhou, X.S., Huang, T.S.: One-class SVM for learning in image retrieval. In: Proc. ICIP (1), pp. 34–37 (2001)

    Google Scholar 

  5. Crucianu, M., Ferecatu, M., Boujemaa, N.: Relevance feedback for image retrieval: a short survey. State of the Art in Audiovisual Content-Based Retrieval. Information Universal Access and Interaction, Including Datamodels and Languages, report of the DELOS2 European Network of Excellence, FP6, 20 (2004)

    Google Scholar 

  6. Dani, V., Hayes, T.P., Kakade, S.M.: Stochastic Linear Optimization under Bandit Feedback. In: Proc. 21st Ann. Conf. on Learning Theory, pp. 355–366 (2008)

    Google Scholar 

  7. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. (2008)

    Google Scholar 

  8. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 Results (2007), http://www.pascal-network.org/challenges/VOC/voc2007/workshop

  9. Fournier, J., Cord, M.: Long-term similarity learning in content-based image retrieval. In: Proc. ICIP (1), pp. 441–444 (2002)

    Google Scholar 

  10. Gosselin, P.-H., Cord, M., Philipp-Foliguet, S.: Active learning methods for Interactive Image Retrieval. IEEE Transactions on Image Processing (2008)

    Google Scholar 

  11. He, X., King, O., Ma, W., Li, M., Zhang, H.: Learning a semantic space from user’s relevance feedback for image retrieval. IEEE Trans. Circuits Syst. Video Techn., 39–48 (2003)

    Google Scholar 

  12. Jing, F., Li, M., Zhang, H., Zhang, B.: A unified framework for image retrieval using keyword and visual features. IEEE Transactions on Image Processing, 979–989 (2005)

    Google Scholar 

  13. Karp, R.M., Kleinberg, R.: Noisy binary search and its applications. In: SODA 2007: Proc. 18th Symp. on Discrete Algorithms, pp. 881–890 (2007)

    Google Scholar 

  14. Koskela, M., Laaksonen, J.: Using Long-Term Learning to Improve Efficiency of Content-Based Image Retrieval. In: Proc. PRIS, pp. 72–79 (2003)

    Google Scholar 

  15. Koskela, M., Laaksonen, J., Oja, E.: Inter-Query Relevance Learning in PicSOM for Content-Based Image Retrieval. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, Springer, Heidelberg (2003)

    Google Scholar 

  16. Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1088–1099 (2006)

    Article  Google Scholar 

  17. Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: State of the art and challenges. TOMCCAP, 1–19 (2006)

    Google Scholar 

  18. Linenthal, J., Qi, X.: An Effective Noise-Resilient Long-Term Semantic Learning Approach to Content-Based Image Retrieval. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2008), Las Vegas, Nevada, USA, March 30-April 4 (2008)

    Google Scholar 

  19. Tao, D., Li, X., Maybank, S.J.: Negative Samples Analysis in Relevance Feedback. IEEE Trans. Knowl. Data Eng. 19(4), 568–580 (2007)

    Article  Google Scholar 

  20. Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Information and Computation, 212–216 (1994)

    Google Scholar 

  21. Pelc, A.: Searching games with errors–fifty years of coping with liars. Theoretical Computer Science, 71-109 (2002)

    Google Scholar 

  22. Tao, D., Tang, X.: Nonparametric Discriminant Analysis in Relevance Feedback for Content-based Image Retrieval. In: IEEE International Conference on Pattern Recognition (ICPR), pp. 1013–1016 (2004)

    Google Scholar 

  23. Rocchio, J.: Relevance Feedback in Information Retrieval. In: Salton: The SMART Retrieval System: Experiments in Automatic Document Processing, ch. 14, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)

    Google Scholar 

  24. Rui, Y., Huang, T.S.: Optimizing Learning in Image Retrieval. In: Proc. CVPR, pp. 1236–1236 (2000)

    Google Scholar 

  25. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. IEEE Trans. Pattern Anal. Mach. Intell., 1349–1380 (2000)

    Google Scholar 

  26. Tong, S., Chang, E.Y.: Support vector machine active learning for image retrieval. In: Proc. ACM Multimedia, pp. 107–118 (2001)

    Google Scholar 

  27. Veltkamp, R.C., Tanase, M.: Content-based Image Retrieval Systems: a Survey. State-of-the-Art in Content-Based Image and Video Retrieval, 97–124 (1999)

    Google Scholar 

  28. Wacht, M., Shan, J., Qi, X.: A Short-Term and Long-Term Learning Approach for Content-Based Image Retrieval. In: Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2006), Toulouse, France, May 14-19, pp. 389–392 (2006)

    Google Scholar 

  29. Zhang, C., Chen, T.: An active learning framework for content-based information retrieval. IEEE Transactions on Multimedia, 260–268 (2002)

    Google Scholar 

  30. Zhou, X.S., Huang, T.S.: Unifying Keywords and Visual Contents in Image Retrieval. In: IEEE MultiMedia, pp. 23–33 (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department Mathematik und Informationstechnologie, Montanuniversität at Leoben, Franz-Josef-Straße 18, 8700, Leoben, Austria

    Peter Auer & Alex Po Leung

Authors
  1. Peter Auer
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  2. Alex Po Leung
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Editor information

Editors and Affiliations

  1. School of Computer Engineering , Nanyang Technological University, 639798, Singapore

    Weisi Lin  & Dacheng Tao  & 

  2. Intelligent Systems Laboratory Systems Research Institute , Polish Academy of Sciences, Poland

    Janusz Kacprzyk

  3. Department of Computing , Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Zhu Li

  4. School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, U.K.

    Ebroul Izquierdo

  5. TCL-Thomson Electronics , Santa Clara, California

    Haohong Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Auer, P., Leung, A.P. (2011). Relevance Feedback Models for Content-Based Image Retrieval. In: Lin, W., Tao, D., Kacprzyk, J., Li, Z., Izquierdo, E., Wang, H. (eds) Multimedia Analysis, Processing and Communications. Studies in Computational Intelligence, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19551-8_2

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  • DOI: https://doi.org/10.1007/978-3-642-19551-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19550-1

  • Online ISBN: 978-3-642-19551-8

  • eBook Packages: EngineeringEngineering (R0)

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