Figure-ground Segmentation using Metrics Adaptation in Level Set Methods
Alexander Denecke, Irene Ayllón Clemente, Heiko Wersing, Julian P. Eggert, Jochen J. Steil · 2010
Abstract. We present an approach for hypothesis-based image segmentation basing 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 fore- and background of an image. We extend this concept towards level set segmentation algorithms, where region descriptors are used to adapt the object contour according to the image features. The fusion of both methods outperforms their individual applications and improve the performance compared to other state of the art segmentation methods. 1