Advertisement

Regularized Semi-supervised Latent Dirichlet Allocation for Visual Concept Learning

  • Liansheng Zhuang
  • Lanbo She
  • Jingjing Huang
  • Jiebo Luo
  • Nenghai Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6523)

Abstract

Topic models are a popular tool for visual concept learning. Current topic models are either unsupervised or fully supervised. Although lots of labeled images can significantly improve the performance of topic models, they are very costly to acquire. Meanwhile, billions of unlabeled images are freely available on the internet. In this paper, to take advantage of both limited labeled training images and rich unlabeled images, we propose a novel technique called regularized Semi-supervised Latent Dirichlet Allocation (r-SSLDA) for learning visual concept classifiers. Instead of introducing a new topic model, we attempt to find an efficient way to learn topic models in a semi-supervised way. r-SSLDA considers both semi-supervised properties and supervised topic model simultaneously in a regularization framework. Experiments on Caltech 101 and Caltech 256 have shown that r-SSLDA outperforms unsupervised LDA, and achieves competitive performance against fully supervised LDA, while sharply reducing the number of labeled images required.

Keywords

Visual Concept Learning Latent Dirichlet Allocation Semi-supervised Learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. JMLR 5, 913–939 (2004)MathSciNetGoogle Scholar
  2. 2.
    Blei, D.M., Jordan, M.I.: Modeling annotated data. In: SIGIR (2003)Google Scholar
  3. 3.
    Bosch, A., Zisserman, A., Munoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their location in images. In: IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 370–377 (2005)Google Scholar
  5. 5.
    Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning Object Categories from Google’s Image Search. In: IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1816–1823 (2005)Google Scholar
  6. 6.
    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. JMLR 3, 993–1022 (2003)zbMATHGoogle Scholar
  7. 7.
    Wang, Y., Mori, G.: Human Action Recognition by Semi-Latent Topic Models. IEEE Trans. on Pattern Analysis and Machine Intelligence Special Issue on Probabilistic Graphical Models in Computer Vision T-PAMI 31(10), 1762–1774 (2009)CrossRefGoogle Scholar
  8. 8.
    Lowe, D.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, ICCV 1999, vol. 2, pp. 1150–1157 (1999)Google Scholar
  9. 9.
    Blei, D., McAuliffe, J.: Supervised topic models. In: Advances in Neural Information Processing Systems, vol. 21 (2007)Google Scholar
  10. 10.
    Wang, C., Blei, D., Fei-Fei, L.: Simultaneous image classification and annotation. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25 (2009)Google Scholar
  11. 11.
    Zhu, X.: Semi-Supervised Learning Literature Survey. Computer Sciences Technical Report 1530, University of Wisconsin-Madison (July 19, 2008)Google Scholar
  12. 12.
    Zhuang, L., She, L., Jiang, Y., Tang, K., Yu, N.: Image Classification via Semi-supervised pLSA. In: Proceedings of the 2009 Fifth International Conference on Image and Graphics (ICIG 2009), September 20-23, pp. 205–208 (2009)Google Scholar
  13. 13.
    Wang, C., Zhang, L., Zhang, H.-J.: Graph-based Multiple-Instance Learning for Object-based Image Retrieval. In: Proceeding of the 1st ACM International Conference on Multimedia Information Retrieval, October 30-31, pp. 156–163 (2008)Google Scholar
  14. 14.
    Zhou, D., Bousquet, O., Lal, N.T., et al.: Learning with local and global consistency. In: NIPS (2003)Google Scholar
  15. 15.
  16. 16.
    Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Computer Vision 45(2), 83–105 (2001)CrossRefzbMATHGoogle Scholar
  17. 17.
    Wang, Y., Mori, G.: Human Action Recognition by Semilatent Topic Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(10), 1762–1774 (2009)CrossRefGoogle Scholar
  18. 18.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Proceeding of IEEE Computer Vision and Pattern Recognition 2004, Workshop on Generative-Model Based Vision (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Liansheng Zhuang
    • 1
    • 2
  • Lanbo She
    • 2
  • Jingjing Huang
    • 2
  • Jiebo Luo
    • 3
  • Nenghai Yu
    • 1
    • 2
  1. 1.MOE-MS Keynote Lab of MCCUSTCHefeiChina
  2. 2.School of Information Science and TechnologyUSTCHefeiChina
  3. 3.Kodak Research LabsEastman Kodak CompanyNew YorkUSA

Personalised recommendations