A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Multivariate Density-Based 3D Shape Descriptors
SMI '07 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2007
Density-based 3D shape descriptors
EURASIP Journal on Applied Signal Processing
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Automated diagnosis of Alzheimer's disease using image similarity and user feedback
Proceedings of the ACM International Conference on Image and Video Retrieval
Similarity Learning for 3D Object Retrieval Using Relevance Feedback and Risk Minimization
International Journal of Computer Vision
Improving 3D similarity search by enhancing and combining 3D descriptors
Multimedia Tools and Applications
Learning kernels on extended Reeb graphs for 3d shape classification and retrieval
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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In this work, we introduce a score fusion scheme to improve the 3D object retrieval performance. The state of the art in 3D object retrieval shows that no single descriptor is capable of providing fine grain discrimination required by prospective 3D search engines. The proposed fusion algorithm linearly combines similarity information originating from multiple shape descriptors and learns their optimal combination of weights by minimizing the empirical ranking risk criterion. The algorithm is based on the statistical ranking framework [CLV07], for which consistency and fast rate of convergence of empirical ranking risk minimizers have been established. We report the results of ontology-driven and relevance feedback searches on a large 3D object database, the Princeton Shape Benchmark. Experiments show that, under query formulations with user intervention, the proposed score fusion scheme boosts the performance of the 3D retrieval machine significantly.