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).