Weakly supervised scale-invariant learning of models for visual recognition

R. Fergus, P. Perona, A. Zisserman

Research output: Contribution to journalArticlepeer-review


We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning is translation and scale-invariant; does not require alignment or correspondence between the training images, and is robust to clutter and occlusion. Category models are probabilistic constellations of parts, and their parameters are estimated by maximizing the likelihood of the training data. The appearance of the parts, as well as their mutual position, relative scale and probability of detection are explicitly described in the model. Recognition takes place in two stages. First, a feature-finder identifies promising locations for the model"s parts. Second, the category model is used to compare the likelihood that the observed features are generated by the category model, or are generated by background clutter. The flexible nature of the model is demonstrated by results over six diverse object categories including geometrically constrained categories (e.g. faces, cars) and flexible objects (such as animals).

Original languageEnglish (US)
Pages (from-to)273-303
Number of pages31
JournalInternational Journal of Computer Vision
Issue number3
StatePublished - Mar 2007


  • Constellation model
  • Object recognition
  • Parts and structure model
  • Semi-supervised learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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