Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
ACM Transactions on Graphics (TOG)
ACM Transactions on Graphics (TOG)
Content based retrieval of VRML objects: an iterative and interactive approach
Proceedings of the sixth Eurographics workshop on Multimedia 2001
A Reflective Symmetry Descriptor for 3D Models
Algorithmica
SMI '04 Proceedings of the Shape Modeling International 2004
ACM SIGGRAPH 2004 Papers
Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Feature Combination and Relevance Feedback for 3D Model Retrieval
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Shape-based retrieval and analysis of 3d models
Communications of the ACM - 3d hard copy
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
On visual similarity based 2D drawing retrieval
Computer-Aided Design
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the international conference on Multimedia
3D object retrieval with bag-of-region-words
Proceedings of the international conference on Multimedia
Intelligent query: open another door to 3d object retrieval
Proceedings of the international conference on Multimedia
ModelSeek: an effective 3D model retrieval system
Multimedia Tools and Applications
Active multiple kernel learning for interactive 3D object retrieval systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
k-Partite graph reinforcement and its application in multimedia information retrieval
Information Sciences: an International Journal
Improving 3D similarity search by enhancing and combining 3D descriptors
Multimedia Tools and Applications
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The technique of relevance feedback has been introduced to content-based 3D model retrieval, however, two essential issues which affect the retrieval performance have not been addressed. In this paper, a novel relevance feedback mechanism is presented, which effectively makes use of strengths of different feature vectors and perfectly solves the problem of small sample and asymmetry. During the retrieval process, the proposed method takes the user's feedback details as the relevant information of query model, and then dynamically updates two important parameters of each feature vector, narrowing the gap between high-level semantic knowledge and low-level object representation. The experiments, based on the publicly available 3D model database Princeton Shape Benchmark (PSB), show that the proposed approach not only precisely captures the user's semantic knowledge, but also significantly improves the retrieval performance of 3D model retrieval. Compared with three state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval effectiveness only with a few rounds of relevance feedback based on several standard measures.