Advertisement

Incorporate Spatial Information into pLSA for Scene Classification

  • Fei Huang
  • Xiaojun Jing
  • Songlin Sun
  • Yueming Lu
Part of the Communications in Computer and Information Science book series (CCIS, volume 320)

Abstract

pLSA has been successfully used in scene classification as an intermediate representation of images, but it didn’t utilize the spatial information of an image which is important for scene classification tasks. To improve the accuracy of classification, we proposed a new method which incorporates spatial information coming from neighbor words and topics’ position into pLSA. Finally, an image can be represented by the position distribution of each latent topic, and subsequently, we train a classifier on the topics’ position distribution vector for each image. Besides, the traditional fold-in heuristic way of pLSA is not necessary and more sophisticated supervised pLSA can be adopted when our no-fold-in way is used, whichalso givesan accuracy improvement.

Keywords

scene classification pLSA spatial information 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: CAIVD Workshop, ICCV (January 1998)Google Scholar
  2. 2.
    Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.J.: Image classification for content-based indexing. IEEE Trans. on Image Processing 10(1), 117–130 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV, pp. 1–22 (2004)Google Scholar
  4. 4.
    Blei, D., Andrew, Y., Jordan, M.: Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1020 (2003)zbMATHGoogle Scholar
  5. 5.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learning 42, 177–196 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Zhai, C.X., Velivelli, A., Yu, B.: A cross-collection mixture model for comparative text mining. In: Proceedings of the Tenth ACM SIGKDD International Conference on KDD, Seattle, WA, USA, August 22-25 (2004)Google Scholar
  7. 7.
    Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering Objects and Their Locations in Images. In: Proc. ICCV, pp. 370–377 (October 2005)Google Scholar
  8. 8.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (June 2006)Google Scholar
  9. 9.
    Fei-Fei, L., Perona, P.: A Bayesian Hierarchical Model for Learning Natural Scene Categories. In: Proc. IEEE CS Conf. CVPR, pp. 524–531 (2005)Google Scholar
  10. 10.
    Quelhas, P., Monay, F., Odobez, J.M., Gatica-Perez, D., Tuytelaars, T., Van Gool, L.: Modeling Scenes with Local Descriptors and Latent Aspects. In: Proc. Int’l Conf. Computer Vision, pp. 883–890 (October 2005)Google Scholar
  11. 11.
    Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning Object Categories from Google’s Image Search. In: Proc. Int’l Conf. Computer Vision, pp. 1816–1823 (October 2005)Google Scholar
  12. 12.
    Bosch, A., Zisserman, A., Munoz, X.: Scene classification using a hybrid generative/dicriminative approach. IEEE Trans. on PAMI (2008)Google Scholar
  13. 13.
    Ergul, E., Arica, N.: Scene Classification Using Spatial Pyramid of Latent Topics. In: ICPR (2010) 1991, 1992 Google Scholar
  14. 14.
    Lowe, D.: Distinctive Image Features from Scale Invariant Keypoints. Int’l J. Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Zhang, E., Mayo, M.: Improving Bag-of-Words Model with Spatial Information. In: Proc. IEEE IVCNZ, pp. 1–6 (2010)Google Scholar
  16. 16.
    Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local Binary Patterns and Its Application to Facial Image Analysis: A Survey. IEEE Transactions on Systems, Man, and Cybernetics 41(6), 765–781 (2011)CrossRefGoogle Scholar
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fei Huang
    • 1
    • 2
  • Xiaojun Jing
    • 1
    • 2
  • Songlin Sun
    • 1
    • 2
  • Yueming Lu
    • 1
    • 2
  1. 1.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Key Laboratory of Trustworthy Distributed Computing and Service(BUPT)Ministry of EducationBeijingChina

Personalised recommendations