A framework for learning to recognize and segment object classes using weakly supervised training data

Abstract

The continual improvement of object recognition systems has resulted in an increased demand for their application to problems which require an exact pixel-level object segmentation. In this paper, we illustrate an example of an object class recognition and segmentation system which is trained using weakly supervised training data, with the goal of examining the influence that different model choices can have on its performance. In order to achieve pixel-level labeling for rigid and deformable objects, we employ regions generated by unsupervised segmentation as the spatial support for our image features, and explore model selection issues related to their representation. Numerical results for pixel-level accuracy are presented on two challenging and varied datasets.