A Discriminative Data-Dependent Mixture-Model Approach for Multiple Instance Learning in Image Classification

  • Qifan Wang
  • Luo Si
  • Dan Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)

Abstract

Multiple Instance Learning (MIL) has been widely used in various applications including image classification. However, existing MIL methods do not explicitly address the multi-target problem where the distributions of positive instances are likely to be multi-modal. This strongly limits the performance of multiple instance learning in many real world applications. To address this problem, this paper proposes a novel discriminative data-dependent mixture-model method for multiple instance learning (MM-MIL) approach in image classification. The new method explicitly handles the multi-target problem by introducing a data-dependent mixture model, which allows positive instances to come from different clusters in a flexible manner. Furthermore, the kernelized representation of the proposed model allows effective and efficient learning in high dimensional feature space. An extensive set of experimental results demonstrate that the proposed new MM-MIL approach substantially outperforms several state-of-art MIL algorithms on benchmark datasets.

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References

  1. 1.
    Andrews, S., Tsochantaridis, I., Hofmann, T.: Support Vector Machines for Multiple-Instance Learning. In: NIPS, pp. 561–568 (2002)Google Scholar
  2. 2.
    Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the Multiple Instance Problem with Axis-Parallel Rectangles. Artif. Intell. 89(1-2), 31–71 (1997)MATHCrossRefGoogle Scholar
  3. 3.
    Fu, Z., Robles-Kelly, A.: An Instance Selection Approach to Multiple Instance Learning. In: CVPR (2009)Google Scholar
  4. 4.
    Hu, Y., Li, M., Yu, N.: Multiple-Instance Ranking: Learning to Rank Images for Image Retrieval. In: CVPR (2008)Google Scholar
  5. 5.
    Zha, Z., Hua, X., Mei, T., Wang, J., Qi, G., Wang, Z.: Joint Multi-Label Multi-Instance Learning for Image Classification. In: CVPR (2008)Google Scholar
  6. 6.
    Qi, G., Hua, X., Rui, Y., Mei, T., Tang, J., Zhang, H.: Concurrent Multiple Instance Learning for Image Categorization. In: CVPR (2007)Google Scholar
  7. 7.
    Chen, Y., Wang, J.Z.: Image Categorization by Learning and Reasoning with Regions. Journal of Machine Learning Research (5), 913–939 (2004)Google Scholar
  8. 8.
    Chen, Y., Bi, J., Wang, J.Z.: MILES: Multiple-Instance Learning via Embedded Instance Selection. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 1931–1947 (2006)CrossRefGoogle Scholar
  9. 9.
    Maron, O., Lozano-Pérez, T.: A Framework for Multiple-Instance Learning. In: NIPS (1997)Google Scholar
  10. 10.
    Zhang, Q., Goldman, S.A.: EM-DD: An Improved Multiple-Instance Learning Technique. In: NIPS (2001)Google Scholar
  11. 11.
    Yang, C., Dong, M., Hua, J.: Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning. In: CVPR (2006)Google Scholar
  12. 12.
    Zhu, J., Hastie, T.: Kernel logistic regression and the import vector machine. Journal of Computational and Graphical Statistics 14(1), 185–205 (2005)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Viola, P.A., Platt, J.C., Zhang, C.: Multiple Instance Boosting for Object Detection. In: NIPS (2005)Google Scholar
  14. 14.
    Rahmani, R., Goldman, S.A.: MISSL: Multiple-Instance Semi-supervised Learning. In: ICML (2006)Google Scholar
  15. 15.
    Maron, O., Ratan, A.L.: Multiple-Instance Learning for Natural Scene Classification. In: ICML (1998)Google Scholar
  16. 16.
    Si, L., Jin, R.: Flexible Mixture Model for Collaborative Filtering. In: ICML, pp. 704–711 (2003)Google Scholar
  17. 17.
    Ray, S., Craven, M.: Supervised Versus Multiple Instance Learning: An Empirical Comparison. In: ICML (2005)Google Scholar
  18. 18.
    Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Mathematical Programming 45(1-3), 503–528 (1989)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Lin, Z., Hua, G., Davis, L.S.: Multiple Instance Feature for Robust Part-based Object Detection. In: CVPR (2009)Google Scholar
  20. 20.
    Schölkopf, B., Herbrich, R., Smola, A.J.: A Generalized Representer Theorem. In: Helmbold, D.P., Williamson, B. (eds.) COLT/ EuroCOLT 2001. LNCS (LNAI), vol. 2111, pp. 416–426. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  21. 21.
    Ray, S., Page, D.: Multiple Instance Regression. In: ICML, pp. 425–432 (2001)Google Scholar
  22. 22.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer Science, Business Media, LLC (2006)Google Scholar
  23. 23.
    Babenko, B., Yang, M.-H., Belongie, S.J.: Visual tracking with online Multiple Instance Learning. In: CVPR (2009)Google Scholar
  24. 24.
    Wang, Q., Tao, L., Di, H.: A Globally Optimal Approach for 3D Elastic Motion Estimation from Stereo Sequences. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 525–538. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  25. 25.
    Leistner, C., Saffari, A., Bischof, H.: MIForests: Multiple-Instance Learning with Randomized Trees. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 29–42. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  26. 26.
    Xue, X., Zhang, W., Zhang, J., Wu, B., Fan, J., Lu, Y.: Correlative Multi-Label Multi-Instance Image Annotation. In: ICCV (2011)Google Scholar
  27. 27.
    Zeisl, B., Leistner, C., Saffari, A., Bischof, H.: On-line Semi-supervised mMultiple-instance Boosting. In: CVPR (2010)Google Scholar
  28. 28.
    Zhang, D., Liu, Y., Si, L., Zhang, J., Lawrence, R.D.: Multiple Instance Learning on Structred Data. In: NIPS (2011)Google Scholar
  29. 29.
    Zhang, D., Wang, F., Si, L., Li, T.: M3IC: Maximum Margin Multiple Instance Clustering. In: IJCAI (2009)Google Scholar
  30. 30.
    Zhang, D., Wang, F., Si, L., Li, T.: Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering. IEEE Transactions on Neural Networks 22(5), 739–751 (2011)CrossRefGoogle Scholar
  31. 31.
    Vezhnevets, A., Buhmann, J.M.: Towards Weakly Supervised Semantic Segmentation by Means of Multiple Instance and Multitask learning. In: CVPR (2010)Google Scholar
  32. 32.
    Zhang, D., Wang, F., Shi, Z., Zhang, C.: Interactive Localized Content-Based Image Retrieval with Multiple Instance Active Learning. Pattern Recognition 43(2), 478–484 (2010)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qifan Wang
    • 1
  • Luo Si
    • 1
  • Dan Zhang
    • 1
  1. 1.Department of Computer SciencePurdue UniversityWest LafayetteUSA

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