Abstract

We present a comparative study on how to use discriminative learn- ing methods such as classi fication, regression, and ranking to address deformable shape segmentation. Traditional generative models and energy minimization methods suffer from local minima. By casting the segmentation into a discrimi- native framework, the target fitting function can be steered to possess a desired shape for ease of optimization yet better characterize the relationship between shape and appearance. To address the high-dimensional learning challenge present in the learning framework, we use a multi-level approach to learning discrimina- tive models. Our experimental results on left ventricle segmentation from ultrasound images and facial feature point localization demonstrate that the dis- criminative models outperform generative models and energy minimization meth- ods by a large margin.