MSc Thesis Defence:STRUCTURAL CO-CONSOLIDATION OF SHAPE COLLECTIONS- Shuyang Sun
We introduce an unsupervised analysis of both homogeneous and heterogeneous shape collections, aiming at organizing shapes based on their similarity in structure. We derive the idea of graph representation of shape structure from previous works and a novel graph editing distance based structure matching cost is defined. For any pair of shapes, we propose a searching scheme to find the best matching pair of graphs with the minimal cost. The core problem is to cluster shapes based on the matching cost, meanwhile, select a set of representative graphs per cluster to exhibit the structural invariants within each cluster and the relationships between clusters. We formulate this problem as a new version of multiple instance clustering where the clustering is coupled with the process of representative selection. We also demonstrate that this multiple instance learning scheme can be used to reveal key semantics of other digital assets in a unsupervised manner.