Efficient Learning of Relational Object Class Models

We present an efficient method for learning part-based object class models from unsegmented images represented as sets of salient features. A model includes parts' appearance, as well as location and scale relations between parts. The object class is generatively modeled using a simple Bayesian network with a central hidden node containing location and scale information, and nodes describing object parts. The model's parameters, however, are optimized to reduce a loss function of the training error, as in discriminative methods. We show how boosting techniques can be extended to optimize the relational model proposed, with complexity linear in the number of parts and the number of features per image. This efficiency allows our method to learn relational models with many parts and features. The method has an advantage over purely generative and purely discriminative approaches for learning from sets of salient features, since generative method often use a small number of part...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall
• Keywords: Computer Vision, Discriminative Approaches, Discriminative Methods, Generative Method, ICCV 2005, Model Includes Parts, Object Parts