Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Curvature scale space image in shape similarity retrieval
Multimedia Systems
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spin Images and Neural Networks for Efficient Content-Based Retrieval in 3D Object Databases
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Shape Topics: A Compact Representation and New Algorithms for 3D Partial Shape Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Content-Based Retrieval of 3-D Objects Using Spin Image Signatures
IEEE Transactions on Multimedia
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Characteristic view is an effective way to represent a 3D object through a set of distinct projections from different view aspects. In this paper, we proposed techniques for automatic characteristic views generations by clustering views of the object from multiple view aspect. By considering the resulting clusters as View Topics that describe a set of portraits of the object, the object can be represented by a set of view topics that can be applied to 3D object retrieval with similarity measures based on the Vector Space Model and the Language Model as well as advanced techniques such as RBF Kernel method. Our experiments have demonstrated that our method is not only invariant with respect to rotation and scaling, but also invariant with respect to the object reflection, and achieve an overall better performance than existing methods.