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Abstract : |
We present a method to learn and recognize object class models ' from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects ' are modeled as ' flexible constellations of parts. A probabilistic representation is' used for all aspects of the object: shape, appearance, occlusion and relative scale. An entropy-based feature detector is ' used to select regions ' and their scale within the image. In learning the parameters ' of the scale-invariant object model are estimated. This ' is ' done using expectation-maximization in a maximum-likelihood setting. In recognition, this ' model is ' used in a Bayesian manner to classify images. The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars') and flexible objects ' (such as animals'). 1., |
