Advertisement

Randomized Probabilistic Latent Semantic Analysis for Scene Recognition

  • Erik Rodner
  • Joachim Denzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

The concept of probabilistic Latent Semantic Analysis (pLSA) has gained much interest as a tool for feature transformation in image categorization and scene recognition scenarios. However, a major issue of this technique is overfitting. Therefore, we propose to use an ensemble of pLSA models which are trained using random fractions of the training data. We analyze empirically the influence of the degree of randomization and the size of the ensemble on the overall classification performance of a scene recognition task. A thoughtful evaluation shows the benefits of this approach compared to a single pLSA model.

Keywords

Random Forest Recognition Rate Visual Word Latent Dirichlet Allocation Ensemble Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Quelhas, P., Monay, F., Odobez, J.M., Gatica-Perez, D., Tuytelaars, T., Van Gool, L.: Modeling scenes with local descriptors and latent aspects. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, pp. 883–890 (2005)Google Scholar
  2. 2.
    Bosch, A., Zisserman, A., Munoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE Trans. Pattern Anal. Mach. Intell. 30(4), 712–727 (2008)CrossRefGoogle Scholar
  3. 3.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1-2), 177–196 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHCrossRefGoogle Scholar
  5. 5.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRefGoogle Scholar
  6. 6.
    Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)CrossRefGoogle Scholar
  7. 7.
    Brants, T., Chen, F., Tsochantaridis, I.: Topic-based document segmentation with probabilistic latent semantic analysis. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 211–218 (2002)Google Scholar
  8. 8.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Lee, D., Seung, H.: Algorithms for non-negative matrix factorization. In: Advances in neural information processing systems, vol. 1998, pp. 556–562. MIT Press, Cambridge (2001)Google Scholar
  10. 10.
    Gaussier, E., Goutte, C.: Relation between plsa and nmf and implications. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 601–602 (2005)Google Scholar
  11. 11.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: Advances in Neural Information Processing Systems, pp. 985–992 (2006)Google Scholar
  13. 13.
    van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluation of color descriptors for object and scene recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  14. 14.
    Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley, Reading (1977)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Erik Rodner
    • 1
  • Joachim Denzler
    • 1
  1. 1.Chair for Computer VisionFriedrich Schiller University of Jena 

Personalised recommendations