New evaluation metrics for mesh segmentation

Abstract

3D model segmentation avails to skeleton extraction, shape partial matching, shape correspondence, texture mapping, shape deformation, and shape annotation. Many excellent solutions have been proposed in the last decade. How to efficiently evaluate these methods and impartially compare their performances are important issues. Since the Princeton segmentation benchmark has been proposed, their four representative metrics have been extensively adopted to evaluate segmentation algorithms. However, comparison to only a fixed ground-truth is problematic because objects have many semantic segmentations, hence we propose two novel metrics to support comparison with multiple ground-truth segmentations, which are named Similarity Hamming Distance (SHD) and Adaptive Entropy Increment (AEI). SHD is based on partial similarity correspondences between automatic segmentation and ground-truth segmentations, and AEI measures entropy change when an automatic segmentation is added to a set of different ground-truth segmentations. A group of experiments demonstrates that the metrics are able to provide relatively higher discriminative power and stability when evaluating different hierarchical segmentations, and also provide an effective evaluation more consistent with human perception.