A random walker based approach to combining multiple segmentations

In this paper we propose an algorithm for combining multiple image segmentations to achieve a final improved segmentation. In contrast to previous works we consider the most general class of segmentation combination, i.e. each input segmentation has an arbitrary number of regions. Our approach is based on a random walker segmentation algorithm which is able to provide high-quality segmentation starting from manually specified seeds. We automatically generate such seeds from an input segmentation ensemble. An information-theoretic optimality criterion is proposed to automatically determine the final number of regions. The experimental results on 300 images with manual ground truth segmentation clearly show the effectiveness of our combination approach.