Content based retrieval of VRML objects: an iterative and interactive approach
Proceedings of the sixth Eurographics workshop on Multimedia 2001
Feature Combination and Relevance Feedback for 3D Model Retrieval
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Center particle swarm optimization
Neurocomputing
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A powerful relevance feedback mechanism for content-based 3D model retrieval
Multimedia Tools and Applications
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Similarity score fusion by ranking risk minimization for 3D object retrieval
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Intelligent query: open another door to 3d object retrieval
Proceedings of the international conference on Multimedia
3D model retrieval using weighted bipartite graph matching
Image Communication
A flexible assembly retrieval approach for model reuse
Computer-Aided Design
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In this study, we present a parallel approach to relevance feedback based on similarity field modification that simultaneously considers all factors affecting the similarity field for 3D model retrieval. First, we present a novel unified mathematical model which formalizes the problem as an optimization problem with multiple objectives and constraints. Secondly, our approach optimizes all the parameters synchronously by treating all the modification operations of the similarity field equally. Thirdly, we improved the standard particle swarm optimization in two different ways. Finally, we present several experiments that show the advantages of our method over existing serial ones.