A Retrieval Matching Method Based Case Learning for 3D Model

Zhi  Liu; Qihua  Chen; Caihong  Xu

doi:10.4236/jsea.2012.57053

Zhi Liu^*, Qihua Chen, Caihong Xu

Abstract: The similarity metric in traditional content based 3D model retrieval method mainly refers the distance metric algorithm used in 2D image retrieval. But this method will limit the matching breadth. This paper proposes a new retrieval matching method based on case learning to enlarge the retrieval matching scope. In this method, the shortest path in Graph theory is used to analyze the similarity how the nodes on the path between query model and matched model effect. Then, the label propagation method and k nearest-neighbor method based on case learning is studied and used to improve the retrieval efficiency based on the existing feature extraction.

Keywords: Case Learning-Based; Retrieval Matching; Similarity Matching

Cite this paper: Z. Liu, Q. Chen and C. Xu, "A Retrieval Matching Method Based Case Learning for 3D Model," Journal of Software Engineering and Applications, Vol. 5 No. 7, 2012, pp. 467-471. doi: 10.4236/jsea.2012.57053.

References

[1] Y. B. Yang, J. Lin and Q. Zhu, “Content-Based 3D Model Retrieval: A Survey,” Chinese Journal of Computers, Vol. 27, No. 10, 2004, pp. 1297-1310.

[2] B. C. Zheng, W. Peng, Y. Zhang, X. Z. Ye and S. Y. Zhang, “A Survey on 3D Model Retrieval Techniques,” Journal of Computer-Aided Design & Computer Graphics, No. 7, 2004.

[3] X. Pan, “Analysis and Retrieval of 3D Model Retrieval shape,” Zhejiang University, Hangzhou, 2005.

[4] C. Y. Cui, “Research on the Key Technology of 3D Model Retrieval,” Zhejiang University, Hangzhou, 2005.

[5] F. Lu, “Shortest Path Algorithms: Taxonomy and Advance in Research,” Acta Geodaeticaet Cartographica Sinica, No. 03, 2001.

[6] M. Wu and R. Jin, “Label Propagation for Classification and Ranking,” Michigan State University, Lansing, 2007.

[7] X. J. Zhu and Z. B. Ghahramani, “Learning from Labeled and Unlabeled Data with Label Propagation. Technical Report CMU-CALD-02-107,” Carnegie Mellon University, Pittsburgh, 2002.

[8] J. Chen, Y. Zhou, B. Wang, L. B. Luo and W. Y. Liu, “Rapid Shape Retrieval Using Improved Graph Transduction,” IEEE Conferences on Information Engineering and Computer Science, Wuhan, 19-20 December 2009, pp. 1-4. doi:10.1109/ICIECS.2009.5366255

[9] T. M. Mitchell, “Machine Learning: Case-Based Learning,” McGraw Hill, New York, 1997.

[10] W. W. Lu and J. Liu, “New Algorithm to Scale up Efficiency of K-Nearest-Neighbor,” Computer Engineering and Applications, No. 4, 2008.

[11] X. Yang, X. Bai, L. J. Latecki and Z. Tu, “Improving Shape Retrieval by Learning Graph Transduction,” ECCV, 2008.

[12] X. Bai, X. W. Yang, L. J. Latecki, W. Y. Liu and Z. w. Tu, “Learning Context-Sensitive Shape Similarity by Graph Transduction,” PAMI.2009.85, 2010, pp. 861-874.

[13] Princeton Shape Retrieval and Analysis Group, 2012. http://www.cs.princeton.edu/gfx/proj/shape/