Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition

  title={Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition},
  author={Rob Fergus and Pietro Perona and Andrew Zisserman},
  journal={International Journal of Computer Vision},
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… CONTINUE READING
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