Adaptive parameter selection for image segmentation based on similarity estimation of multiple segmenters

Abstract

This paper addresses the parameter selection problem in image segmentation. Mostly, segmentation algorithms have parameters which are usually fixed beforehand by the user. Typically, however, each image has its own optimal set of parameters and in general a fixed parameter setting may result in unsatisfactory segmentations. In this paper we present a novel unsupervised framework for automatically choosing parameters based on a comparison with the results from some reference segmentation algorithm(s). The experimental results show that our framework is even superior to supervised selection method based on ground truth. The proposed framework is not bounded to image segmentation and can be potentially applied to solve the adaptive parameter selection problem in other contexts. © 2011 Springer-Verlag Berlin Heidelberg.