Visual Object Category Recognition

We investigate two generative probabilistic models for category-level object recognition. Both schemes are designed to learn categories with a minimum of supervision, requiring only a set of images known to contain the target category from a similar viewpoint. In both methods, learning is translation and scale-invariant; does not require alignment or correspondence between the training images, and is robust to clutter and occlusion. The schemes are also robust to heavy contamination of the training set with unrelated images, enabling them to learn directly from the output of Internet Image Search engines.In the first approach, 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 represented. 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.