Figure-ground Segmentation using Metrics Adaptation in Level Set Methods

We present an approach for hypothesis-based image segmentation founding on the integration of level set methods and discriminative feature clustering techniques. Building up on previous work, we investigate Localized Generalized Matrix Learning Vector Quantization (LGMLVQ) to train a classifier for foreand background of an image. Here we extend this concept towards level set segmentation algorithms, where region descriptors are used to adapt the object contour according to the image features. Finally we demonstrate that the fusion of both methods is capable to outperform their individual applications and improve the performance compared to other state of the art segmentation methods.