Abstract. This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned ...
Jamie Shotton, John M. Winn, Carsten Rother, Anton...
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
Most object recognition systems require large databases of real images for classifier training. To collect real images for this purpose is a difficult and expensive process. This ...
We report an improved methodology for training classifiers for document image content extraction, that is, the location and segmentation of regions containing handwriting, machine...
This paper describes an ongoing collaborative research program between the Computer Science and the Forestry and Wildlife Management Departments at the University of Massachusetts...
Howard J. Schultz, Dana Slaymaker, Chris Holmes, F...