Advertisement

Unsupervised Classemes

  • Claudio Cusano
  • Riccardo Satta
  • Simone Santini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

In this paper we present a new model of semantic features that, unlike previously presented methods, does not rely on the presence of a labeled training data base, as the creation of the feature extraction function is done in an unsupervised manner.

We test these features on an unsupervised classification (clustering) task, and show that they outperform primitive (low-level) features, and that have performance comparable to that of supervised semantic features, which are much more expensive to determine relying on the presence of a labeled training set to train the feature extraction function.

Keywords

Feature Vector Semantic Feature Multiple Kernel Learning Sift Descriptor Primitive Feature 
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.
Download to read the full conference paper text

References

  1. 1.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using rois and multiple kernel learning. International Journal of Computer Vision 2008, 1–25 (2008)Google Scholar
  2. 2.
    Chatzichristofis, S.A., Boutalis, Y.S.: CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 312–322. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Ciocca, G., Cusano, C., Santini, S., Schettini, R.: Prosemantic Features for Content-Based Image Retrieval. In: Detyniecki, M., García-Serrano, A., Nürnberger, A. (eds.) AMR 2009. LNCS, vol. 6535, pp. 87–100. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Ciocca, G., Cusano, C., Santini, S., Schettini, R.: Supervised features for unsupervised image categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted, 2012)Google Scholar
  5. 5.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  6. 6.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  7. 7.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 221–228 (2009)Google Scholar
  9. 9.
    Hoiem, D., Efros, A.A., Hebert, M.: Automatic photo pop-up 24(3), 577–584 (2005)Google Scholar
  10. 10.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)Google Scholar
  11. 11.
    Li, L.J., Fei-Fei, L.: What, where and who? classifying events by scene and object recognition. In: Proc. IEEE Int’l Conf. Computer Vision, pp. 1–8 (2007)Google Scholar
  12. 12.
    Li, L.J., Su, H., Xing, E.P., Fei-Fei, L.: Object bank: A high-level image representation for scene classification and semantic feature sparsification. In: Advances in Neural Information Processing Systems (2010)Google Scholar
  13. 13.
    Naphade, M., Smith, J.R., Tesic, J., Chang, S.F., Hsu, W., Kennedy, L., Hauptmann, A., Curtis, J.: Large-scale concept ontology for multimedia. IEEE Multimedia 13(3), 86–91 (2006)CrossRefGoogle Scholar
  14. 14.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. Int’l J. Computer Vision 42(3), 145–175 (2001)zbMATHCrossRefGoogle Scholar
  15. 15.
    Oliva, A., Torralba, A.: Building the gist of a scene: The role of global image features in recognition. Progress in Brain Research 155, 23–36 (2006)CrossRefGoogle Scholar
  16. 16.
    Sikora, T.: The MPEG-7 visual standard for content description-an overview. IEEE Trans. Circuits and Systems for Video Technology 11(6), 696–702 (2001)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Torresani, L., Szummer, M., Fitzgibbon, A.: Efficient Object Category Recognition Using Classemes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 776–789. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)CrossRefGoogle Scholar
  20. 20.
    Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. the Am. Statistical Assoc. 58(301), 236–244 (1963)CrossRefGoogle Scholar
  21. 21.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73(2), 213–238 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Claudio Cusano
    • 1
  • Riccardo Satta
    • 2
  • Simone Santini
    • 3
  1. 1.Department of Informatics, Systems and Communication (DISCo)Università degli Studi di Milano-BicoccaMilanoItaly
  2. 2.Department of Electrical and Electronic EngineeringUniversità di CagliariItaly
  3. 3.Escuela Politécnica SuperiorUniversidad Autónoma de MadridSpain

Personalised recommendations

Cite paper