Advertisement

Abstract

We describe an annotation and retrieval framework that uses a semantic image representation by contextual modeling of images using occurrence probabilities of concepts and objects. First, images are segmented into regions using clustering of color features and line structures. Next, each image is modeled using the histogram of the types of its regions, and Bayesian classifiers are used to obtain the occurrence probabilities of concepts and objects using these histograms. Given the observation that a single class with the highest probability is not sufficient to model image content in an unconstrained data set with a large number of semantically overlapping classes, we use the concept/object probabilities as a new representation, and perform retrieval in the semantic space for further improvement of the categorization accuracy. Experiments on the TRECVID and Corel data sets show good performance.

Keywords

Line Segment Contextual Modeling Image Annotation Semantic Space Probabilistic Latent Semantic Analysis 
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.: A thousand words in a scene. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(9), 1575–1589 (2007)CrossRefGoogle Scholar
  2. 2.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  3. 3.
    van Gemert, J.C., Geusebroek, J., Veenman, C.J., Snoek, C.G.M., Smeulders, A.W.M.: Robust scene categorization by learning image statistics in context. In: CVPR (2006)Google Scholar
  4. 4.
    Vogel, J., Schiele, B.: Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision 72(2), 133–157 (2007)CrossRefGoogle Scholar
  5. 5.
    Li, Y., Shapiro, L.G., Bilmes, J.A.: A generative/discriminative learning algorithm for image classification. In: ICCV (2005)Google Scholar
  6. 6.
    Paclik, P., Duin, R.P.W., van Kempen, G.M.P., Kohlus, R.: Segmentation of multi-spectral images using the combined classifier approach. Image and Vision Computing 21(6), 473–482 (2003)CrossRefGoogle Scholar
  7. 7.
    Li, Y., Shapiro, L.G.: Consistent line clusters for building recognition in CBIR. In: ICPR (2002)Google Scholar
  8. 8.
    Mojena, R.: Hierarchical grouping methods and stopping rules: An evaluation. The Computer Journal 20(4), 359–363 (1977)CrossRefzbMATHGoogle Scholar
  9. 9.
    Gokalp, D., Aksoy, S.: Scene classification using bag-of-regions representations. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Beyond Patches Workshop, Minneapolis, Minnesota, June 23 (2007)Google Scholar
  10. 10.
    Tax, D.M.J.: One-Class Classification. PhD thesis, Delft University of Technology, Delft, The Netherlands (2001)Google Scholar
  11. 11.
    Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54(1), 45–66 (2004)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Özge Çavuş
    • 1
  • Selim Aksoy
    • 1
  1. 1.Department of Computer EngineeringBilkent UniversityAnkaraTurkey

Personalised recommendations