On the definition of Shape Parts: a Dominant Sets Approach

TL;DR

This paper introduces a novel graph-based method for shape decomposition that transforms shapes into visibility graphs, enhances them with diffusion, and uses a modified dominant sets algorithm to automatically identify meaningful shape parts.

Contribution

It proposes a new approach combining visibility graphs, diffusion processes, and a modified dominant sets algorithm for automatic shape part detection, improving robustness and effectiveness.

Findings

Effective shape decomposition on multiple databases

Automatic determination of the number of shape parts

Robustness against shape variability

Abstract

In the present paper a novel graph-based approach to the shape decomposition problem is addressed. The shape is appropriately transformed into a visibility graph enriched with local neighborhood information. A two-step diffusion process is then applied to the visibility graph that efficiently enhances the information provided, thus leading to a more robust and meaningful graph construction. Inspired by the notion of a clique as a strict cluster definition, the dominant sets algorithm is invoked, slightly modified to comport with the specific problem of defining shape parts. The cluster cohesiveness and a node participation vector are two important outputs of the proposed graph partitioning method. Opposed to most of the existing techniques, the final number of the clusters is determined automatically, by estimating the cluster cohesiveness on a random network generation process. Experimental results on several shape databases show the effectiveness of our framework for graph based shape decomposition.