Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
A new 3D model retrieval approach based on the elevation descriptor
Pattern Recognition
Bipartite graph reinforcement model for web image annotation
Proceedings of the 15th international conference on Multimedia
A powerful relevance feedback mechanism for content-based 3D model retrieval
Multimedia Tools and Applications
Visual tag dictionary: interpreting tags with visual words
WSMC '09 Proceedings of the 1st workshop on Web-scale multimedia corpus
MM '09 Proceedings of the 17th ACM international conference on Multimedia
3D model comparison using spatial structure circular descriptor
Pattern Recognition
View-based 3D model retrieval with probabilistic graph model
Neurocomputing
A 3D Shape Retrieval Framework Supporting Multimodal Queries
International Journal of Computer Vision
PANORAMA: A 3D Shape Descriptor Based on Panoramic Views for Unsupervised 3D Object Retrieval
International Journal of Computer Vision
ModelSeek: an effective 3D model retrieval system
Multimedia Tools and Applications
Mediapedia: mining web knowledge to construct multimedia encyclopedia
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
A Bayesian 3-D Search Engine Using Adaptive Views Clustering
IEEE Transactions on Multimedia
Content-Based Retrieval of 3-D Objects Using Spin Image Signatures
IEEE Transactions on Multimedia
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
3D model retrieval using weighted bipartite graph matching
Image Communication
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In this paper, we propose a multi-bipartite graph reinforcement model for representative views re-ranking in 3D model retrieval. Given the views of one query 3D model, all query views are grouped into clusters to generate representative views and corresponding original weights. In the retrieval procedure, labeled positive retrieval results are employed to refine the query information. Each group of views from positive retrieval results and the group of representative query views are employed to construct a bipartite graph, and a multi-bipartite graph reinforcement algorithm is performed on these bipartite graphs to re-rank all views. Then the weights of all representative query views are updated. Experimental results on two 3D model databases are provided to justify the effectiveness of the proposed method.