Localized Content-Based Image Retrieval Using Semi-Supervised Multiple Instance Learning

  • Dan Zhang
  • Zhenwei Shi
  • Yangqiu Song
  • Changshui Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)


In this paper, we propose a Semi-Supervised Multiple-Instance Learning (SSMIL) algorithm, and apply it to Localized Content-Based Image Retrieval(LCBIR), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comparison result of SSMIL with several state-of-art algorithms is promising.


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  1. 1.
    Dietterich, T.G., Lathrop, R.H., Lozano-Përez, T.: Solving the Multiple Instance Problem with Axis-Parallel Rectangles. Artificial Inteligence 1446, 1–8 (1998)Google Scholar
  2. 2.
    Maron, O., Lozano-Përez, T.: A Framework for Multiple-Instance Learning. Advances in Neural Information Processing System 10, 570–576 (1998)Google Scholar
  3. 3.
    Maron, O., Ratan, A.L.: Multiple-Instance Learning for Natural Scene Classification. In: Proc. 15th Int’l. Conf. Machine Learning, pp. 341–349 (1998)Google Scholar
  4. 4.
    Chen, Y., Wang, J.Z.: Image Categorization by Learning and Reasoning with Regions. J. Machine Learning Research 5, 913–939 (2004)Google Scholar
  5. 5.
    Chen, Y., Bi, J., Wang, J.Z.: MILES: Multiple-Instance Learning via Embedded Instance Selection. IEEE Transatctions on Pattern Analysis and Machine Intelligence 28(12) (2006)Google Scholar
  6. 6.
    Zhang, Q., Goldman, S.: EM-DD: An improved Multiple-Instance Learning. In: Advances in Neural Information Processing System, vol. 14, pp. 1073–1080 (2002)Google Scholar
  7. 7.
    Rahmani, R., Goldman, S., Zhang, H., et al.: Localized Content-Based Image Retrieval. In: Proceedings of ACM Workshop on Multimedia Image Retrieval, ACM Press, New York (2005)Google Scholar
  8. 8.
    Rahmani, R., Goldman, S.: MISSL: Multiple-Instance Semi-Supervised Learning. In: Proc. 23th Int’l. Conf. Machine Learning, pp. 705–712 (2006)Google Scholar
  9. 9.
    Cheung, P.-M., Kwok, J.T.: A Regularization Framework for Multiple-Instance Learning. In: ICML (2006)Google Scholar
  10. 10.
    Andrews, S., Tsochantaridis, I., Hofmann, T.: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing System, vol. 15, pp. 561–568 (2003)Google Scholar
  11. 11.
    Andrews, S., Hofmann, T.: Multiple Instance Learning via Disjunctive Programming Boosting. In: Advances in Neural Information Processing System, vol. 16, pp. 65–72 (2004)Google Scholar
  12. 12.
    Joachims, T.: Transductive Inference for Text Classification using Support Vector Machine. In: Proc. 16th Int’l. Conf. Machine Learning, pp. 200–209 (1999)Google Scholar
  13. 13.
    Bennett, K.P., Demiriz, A.: Semi-supervised sup- port vector machines. In: Advances in Neural Information Processing System, vol. 11, pp. 368–374 (1999)Google Scholar
  14. 14.
    Zhu, X.: Semi-supervised learning literature survey, in Technical Report 1530, Department of Computer Sci- ences, University of Wisconsin at Madison (2006)Google Scholar
  15. 15.
    Zhou, Z.H., Zhang, M.L.: Multi-Instance Multi-Label Learning with Application to Scene Classification. In: Advances in Neural Information Processing System (2006)Google Scholar
  16. 16.
    Csurka, G., Bray, C., Dance, C., Fan, L.: Visual Categorization with Bags of Keypoints. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 59–74. Springer, Heidelberg (2004)Google Scholar
  17. 17.
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dan Zhang
    • 1
  • Zhenwei Shi
    • 2
  • Yangqiu Song
    • 1
  • Changshui Zhang
    • 1
  1. 1.State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation,Tsinghua University, Beijing 100084China
  2. 2.Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083P.R. China

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