SVM-based Semantic Clustering and Retrieval of A 3D Model Database

In this paper, we present a semi-supervised semantic clustering method based on Support Vector Machines (SVM) to organize the 3D models semantically. Ground truth data is used to identify the pattern of each semantic category by supervised learning. Unknown data is then automatically classified and clustered based on the resulting pattern. We also propose a unified search strategy which applies semantic constraints to the retrieval by using the resulting clusters. A query is first labeled with its semantic concept so that shape-based search is only conducted in the corresponding cluster. Experiments are performed to evaluate the effects of semantic clustering and retrieval respectively by using our prototypical 3D Engineering Shape Search System (3DESS).