Unsupervised Learning of Characteristic Object Parts from Videos

object class model from videos in an unsupervised manner. The model consists of a small set of object parts, each of them highly characteristic for a particular object view. We obtain these parts by automatically determining those object regions that are similar in different object instances. Using such a model we are able to detect objects in arbitrary poses. We successfully validate our approach using the PASCAL Visual Object Challenge 2006 [2] image database which shows promising results. Compared to state of the art approaches we keep the performance while training is done without supervision.