Automatic selection and combination of descriptors for effective 3D similarity search

We focus on improving the effectiveness of similarity search in 3D object repositories from a system-oriented perspective. Motivated by an effectiveness evaluation of several individual 3D retrieval methods, we research a selection heuristic, called purity, for choosing retrieval methods based on query-dependent characteristics. We show that the purity selection method significantly improves the search effectiveness compared to the best single methods. We then show that retrieval effectiveness can be further boosted by considering combinations of multiple retrieval methods to perform the search. We propose to use a dynamically weighted combination of feature vectors based on the purity concept, and we experimentally show that the search effectiveness of our combined methods by far exceeds the effectiveness of our best implemented single method.