Semi-supervised learning and recognition of object classes
We present a method for learning object classes without supervision: learning is scale and translation-invariant, does not require alignment nor correspondence between the training images, and is robust to clutter and occlusion. Class 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. We investigate two models differing in their representation of the part’s configuration. Parts may be regions, representing object appearance patches, or curves representing the local object shape. Recognition takes place in two stages. First, a feature-finder identifies promising locations for the models’ part. Second, the category model is used to compare the likelihood that the observed features are generated by the model, or is generated by background clutter. The flexible nature of the model is demonstrated by excellent results over six diverse object classes including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals). We also describe an application of a learnt model to object based retrieval of frames from the situation comedy ‘Fawlty Towers’.