3D Shapes Co-segmentation by Combining Fuzzy C-Means with Random Walks

Abstract

Co-segmentation of 3D shapes has been receiving increasing attention, and treated as clustering problem in a descriptor space by a few unsupervised approaches to achieve proper co-segmentation of shapes with large variability. However, most of the existing algorithms are performed on segment level and heavily dependent on the per-object segmentation. Accordingly, we propose a co-segmentation method based on combination of Fuzzy C-Means (FCM) and Random Walks together. The novelty of our method is twofold. As an efficient soft clustering algorithm, FCM is firstly used to cluster directly all the facets in the set in terms of their shape descriptors. The clusters of facets are created as candidates of the consistent parts of shapes. Random Walks model is then incorporated into the iterations of FCM clustering to adjust the assignment of facets in each candidate according to the minima rule of shape segmentation. The results of co-segmentation are refined through the iterations of FCM until its convergence conditions are satisfied. Experiments prove that the method proposed in this paper can not only get more stable results without per-object segmentation, but also improve the accuracy of cosegmentation.

Part‐Based Mesh Segmentation: A Survey

“…Zhang et al . [ZSS*13] proposed a co‐segmentation method based on soft clustering in the descriptor space. It uses a concatenated feature vector per face, which consists of the following four shape descriptors: GC, SDF, AGD, and SC.…”

Feature Driven Learning Techniques for 3D Shape Segmentation

“…the number of bins) impacts the level of detail captured by the feature. SC has been widely adopted in segmentation work over the years, with both supervised [3,39,246] and unsupervised [36,37,46,247,248] methods utilising it.…”