Shared Features for Multiclass Object Detection

  • Antonio Torralba
  • Kevin P. Murphy
  • William T. Freeman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)


We consider the problem of detecting a large number of different classes of objects in cluttered scenes. We present a learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). Shared features, emerge in a model of object recognition trained to detect many object classes efficiently and robustly, and are preferred over class-specific features. Although that class-specific features achieve a more compact representation for a single category, the whole set of shared features is able to provide more efficient and robust representations when the system is trained to detect many object classes than the set of class-specific features. Classifiers based on shared features need less training data, since many classes share similar features (e.g., computer screens and posters can both be distinguished from the background by looking for the feature “edges in a rectangular arrangement”).


Object Recognition Object Detection Object Class Shared Feature Weak Learner 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(11), 1475–1490 (2004)CrossRefGoogle Scholar
  2. 2.
    Bart, E., Ullman, S.: Cross-generalization: learning novel classes from a single example by feature replacement. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2005)Google Scholar
  3. 3.
    Caruana, R.: Multitask learning. Machine Learning 28(1), 41–75 (1997)CrossRefGoogle Scholar
  4. 4.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2003)Google Scholar
  5. 5.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of statistics 28(2), 337–374 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Heisele, B., Serre, T., Mukherjee, S., Poggio, T.: Feature reduction and hierarchy of classifiers for fast object detection in video images. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2001)Google Scholar
  7. 7.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 1Google Scholar
  8. 8.
    Krempp, S., Geman, D., Amit, Y.: Sequential learning of reusable parts for object detection. Technical report, CS Johns Hopkins (2002),
  9. 9.
    Lazebnik, S., Schmid, C., Ponce, J.: Affine-invariant local descriptors and neighborhood statistics for texture recognition. In: Intl. Conf. on Computer Vision (2003)Google Scholar
  10. 10.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  11. 11.
    LeCun, Y., Huang, F.-J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of CVPR 2004. IEEE Press, Los Alamitos (2004)Google Scholar
  12. 12.
    Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2003), Madison, WI (June 2003)Google Scholar
  13. 13.
    Levi, K., Fink, M., Weiss, Y.: Learning from a small number of training examples by exploiting object categories. In: Workshop of Learning in Computer Vision (2004)Google Scholar
  14. 14.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: DAGM 25th Pattern Recognition Symposium (2003)Google Scholar
  15. 15.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. of the International Conference on Computer Vision ICCV, Corfu, pp. 1150–1157 (1999)Google Scholar
  16. 16.
    Murase, H., Nayar, S.: Visual learning and recognition of 3-d objects from appearance. Intl. J. Computer Vision 14, 5–24 (1995)CrossRefGoogle Scholar
  17. 17.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. Intl. J. Computer Vision 38(1), 15–33 (2000)zbMATHCrossRefGoogle Scholar
  18. 18.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. MIT AI Lab Memo AIM-2005-025 (September 2005)Google Scholar
  19. 19.
    Schapire, R., Singer, Y.: BoosTexter: A boosting-based system for text categorization. Machine Learning 39(2/3), 135–168 (2000)zbMATHCrossRefGoogle Scholar
  20. 20.
    Schapire, R.: The boosting approach to machine learning: An overview. In: MSRI Workshop on Nonlinear Estimation and Classification (2001)Google Scholar
  21. 21.
    Schneiderman, H., Kanade, T.: A statistical model for 3D object detection applied to faces and cars. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2000)Google Scholar
  22. 22.
    Sudderth, E., Torralba, A., Freeman, W.T., Willsky, A.: Learning hierarchical models of scenes, objects, and parts. In: IEEE Conf. on Computer Vision and Pattern Recognition (2005)Google Scholar
  23. 23.
    Thrun, S., Pratt, L. (eds.): Machine Learning. Special issue on Inductive Transfer (1997)Google Scholar
  24. 24.
    Torralba, A., Murphy, K., Freeman, W.: Sharing features: efficient boosting procedures for multiclass object detection. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2004)Google Scholar
  25. 25.
    Vidal-Naquet, M., Ullman, S.: Object recognition with informative features and linear classification. In: IEEE Conf. on Computer Vision and Pattern Recognition (2003)Google Scholar
  26. 26.
    Viola, P., Jones, M.: Robust real-time object detection. Intl. J. Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Antonio Torralba
    • 1
  • Kevin P. Murphy
    • 2
  • William T. Freeman
    • 1
  1. 1.Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyUSA
  2. 2.Departments of computer science and statisticsUniversity of British ColumbiaCanada

Personalised recommendations