2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (2005)
San Diego, California
June 20, 2005 to June 26, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2005.250
Aharon Bar-Hillel , Hebrew University of Jerusalem
Tomer Hertz , Hebrew University of Jerusalem
Daphna Weinshall , Hebrew University of Jerusalem
We propose a new technique for object class recognition, which learns a generative appearance model in a discriminative manner. The technique is based on the intermediate representation of an image as a set of patches, which are extracted using an interest point detector. The learning problem becomes an instance of supervised learning from sets of unordered features. In order to solve this problem, we designed a classifier based on a simple, part based, generative object model. Only the appearance of each part is modeled. When learning the model parameters, we use a discriminative boosting algorithm which minimizes the loss of the training error directly. The models thus learnt have clear probabilistic semantics, and also maintain good classification performance. The performance of the algorithm has been tested using publicly available benchmark data, and shown to be comparable to other state of the art algorithms for this task; our main advantage in these comparisons is speed (order of magnitudes faster) and scalability.
T. Hertz, D. Weinshall and A. Bar-Hillel, "Object Class Recognition by Boosting a Part-Based Model," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)(CVPR), San Diego, CA, USA USA, 2005, pp. 702-709.