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Multiple-Instance Active Learning for Image Categorization

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Advances in Multimedia Modeling (MMM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5371))

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Abstract

Both multiple-instance learning and active learning are widely employed in image categorization, but generally they are applied separately. This paper studies the integration of these two methods. Different from typical active learning approaches, the sample selection strategy in multiple-instance active learning needs to handle samples in different granularities, that is, instance/region and bag/image. Three types of sample selection strategies are evaluated: (1) selecting bags only; (2) selecting instances only; and (3) selecting both bags and instances. As there is no existing method for the third case, we propose a set kernel based classifier, based on which, a unified bag and/or instance selection criterion and an integrated learning algorithm are built. The experiments on Corel dataset show that selecting both bags and instances outperforms the other two strategies.

This work was performed when Dong Liu was visiting Microsoft Research Asia as an intern.

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References

  1. Maron, O., Ratan, L.: Multiple-Instance learning for natural scene classification. In: Proceedings of the 15th International Conference on Machine Learning, pp. 341–349 (1998)

    Google Scholar 

  2. Bunescu, R.C., Mooney, R.J.: Multiple Instance Learning for Sparse Positive Bags. In: Proceedings of the 24th International Conference on Machine Learning, pp. 105–112 (2007)

    Google Scholar 

  3. Settles, B., Craven, M., Ray, S.: Multiple-Instance Active Learning. In: Advances in Neural Information Processing Systems (NIPS), vol. 20, pp. 1289–1296 (2007)

    Google Scholar 

  4. Gärtner, T., Flach, P., Kowalczyk, A., Smola, A.: Multi-Instance Kernels. In: Proceedings of 19th International Conference on Machine Learning, pp. 179–186 (2002)

    Google Scholar 

  5. Tong, S., Koller, D.: Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research 2, 45–66 (2001)

    MATH  Google Scholar 

  6. Tong, S., Chang, E.: Support Vector Machine Active Learning for Image Retrieval. In: Proceedings of the ninth ACM international conference on Multimedia, pp. 107–118 (2004)

    Google Scholar 

  7. Brinker, K.: Incorporating Diversity in Active Learning with Support Vector Machines. In: Proceedings of the 20th International Conference on Machine Learning, pp. 59–66 (2003)

    Google Scholar 

  8. Cohn, D., Ghahramani, Z., Jordan, M.: Active Learning with Statistical Models. Journal of Artificial Intelligence Research 4, 129–145 (1996)

    MATH  Google Scholar 

  9. Chang, C., Lin, C.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libSVM

  10. Yuan, J., Li, J., Zhang, B.: Exploiting Spatial Context Constraints for Automatic Image Region Annotation. In: Proceeding of ACM Multimedia, pp. 595–604 (2007)

    Google Scholar 

  11. Deng, Y., Manjunath, B.S.: Unsupervised Segmentation of Color-Texture Regions in Images and Video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800–810 (2001)

    Article  Google Scholar 

  12. Fawcett, T.: An introduction to ROC Analysis. Pattern Recognition Letters 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  13. Baram, Y., Yaniv, R.E., Luz, K.: Online Choice of Active Learning Algorithms. Journal of Machine Learning Researh 5, 255–291 (2004)

    MathSciNet  Google Scholar 

  14. Taylor, J.S., Christianini, N.: On the Generalization of Soft Margin Algorithms. IEEE Transaction on Information Theory 48(10), 2721–2735 (2002)

    Article  MathSciNet  MATH  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Liu, D., Hua, XS., Yang, L., Zhang, HJ. (2009). Multiple-Instance Active Learning for Image Categorization. In: Huet, B., Smeaton, A., Mayer-Patel, K., Avrithis, Y. (eds) Advances in Multimedia Modeling . MMM 2009. Lecture Notes in Computer Science, vol 5371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92892-8_27

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  • DOI: https://doi.org/10.1007/978-3-540-92892-8_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92891-1

  • Online ISBN: 978-3-540-92892-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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