In this paper, we present a novel distance metric called Segmentation Edit Distance (SED) and its use as a segmentation evaluation metric. In segmentation evaluation, the difference or distance of a test segmentation and the associated ground truth segmentation are measured in order to compare different algorithms. Our proposed edit distance extends the idea of other edit distances such as the string edit distance or the graph edit distance to the domain of image segmentations. The distance is based on the cost of edit operations that are needed to transform one segmentation into another. Only one edit operation, the deletion of an error region, is considered. Different to other edit distances, the costs assigned to this operation are based on properties of the error regions and the image processing method used to delete a region. As a segmentation evaluation metric, it combines the assessment of accuracy and efficiency into a single metric. Evaluations on synthetic and real world data show promising results compared to other state of the art segmentation evaluation metrics.


Pucher, D., & Kropatsch, W. (2018). Segmentation Edit Distance. In IAPR/ICPR 2018 International Conference on Pattern Recognition (pp. 1175–1185). IEEE Computer Society. http://hdl.handle.net/20.500.12708/57534