Using Filtering Algorithm for Partial Similarity Search on 3D Shape Retrieval System
Because 3D models are increasingly created and designed using computer graphics, computer vision, CAD medical imaging, and a variety of other applications, a large number of 3D models are being shared and offered on the Web. Large databases of 3D models, such as the Princeton Shape Benchmark Database , the 3D Cafe repository , and Aim@Shape network , are now publicly available. These datasets are made up of contributions from the CAD community, computer graphics artists, and the scientific visualization community. The problem of searching for a specific shape in a large database of 3D models is an important area of research. Text descriptors associated with 3D shapes can be used to drive the search process , as is the case for 2D images . However, text descriptions may not be available, and furthermore may not apply for part-matching or similarity-based matching. Several content-based 3D shape retrieval algorithms have been proposed [6–8].
In this chapter, we propose a novel filtering method to filter out shapes. The proposed method is based on geometrical information rather than on topological information alone. Shapes are removed from the candidate pool if the processing part of the key shape is not similar to any part of the potential candidate shape. We select as partly similar shapes, those shapes that have the greatest number of similar parts. In addition, the method is herein implemented on a curve-skeleton thickness histogram (CSTH)  based 3D shape search. Therefore, the method can also be easily implemented on other multibranch complex graph matching applications if there are heavy values on the curves.
The remainder of the chapter is organized as follows. Section 34.2 provides an overview of related work in skeleton generation and content-based retrieval. In Sect. 34.3 we describe the CSTH. In addition, we describe the segment thickness histogram (STH) and the postprocession of CSTH to produce STH. In Sect. 34.4, we describe the novel filtering algorithm and the partial similarity shape retrieval method based on the shape’s STHs mentioned in Sect. 34.3. A discussion of an empirical study and the results thereof are presented in Sect. 34.5. Finally, in Sect. 34.6, we conclude the chapter and present ideas for future study.
KeywordsTopological Information Filter Algorithm Partial Similarity Shape Retrieval Candidate Pool
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- 1.P. Shilane, M. K. P. Min, and T. Funkhouser: The Princeton shape benchmark, Shape Modeling International, Genoa, June 2004.Google Scholar
- 2.3D Cafe: http://www.3dcafe.com/asp/freestuff.asp.
- 3.AIM@SHAPE Network of Excellence: http://www.aimatshape.net/.
- 4.Princeton Shape Retrieval and Analysis: 3D Model Search: http://shape.cs.princeton.edu/ search.html.
- 5.Google Image Search: http://www.google.com/.
- 6.H. Sundar, D. Silver, N. Gagvani, and S. Dickenson: Skeleton based shape matching and retrieval, SMI 2003, pp. 130–139, 2003.Google Scholar
- 7.M. Hilaga, Y. Shinagawa, T. Kohmura, and T. Kunii: Topology matching for fully automatic similarity estimation of 3D shapes, ACM SIGGRAPH 2001 Proceedings, 2001.Google Scholar
- 8.R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin: Matching 3D Models with Shape Distributions Shape Modeling International, Genova, Italy, May 2001.Google Scholar
- 9.N. D. Cornea, M. F. Demirci, D. Silver, A. Shokoufandeh, S. J. Dickinson, and P. B. Kantor: 3D object retrieval using many-to-many matching of curve skeletons, International Conference on Shape Modeling and Applications SMI 2005, MIT, Boston, June 15–17, 2005.Google Scholar
- 10.Y. Lu, K. Kaneko, and A. Makinouchi: 3D shape matching using curve-skeletons with thickness, 1st International Workshop on Shapes and Semantics, June 2006.Google Scholar
- 11.K. Siddiqi, A. Shokoufandeh, S. Dickinson, and S. Zucker: Shock graphs and shape matching, International Journal of Computer Vision, 30: 1–24, 1999.Google Scholar
- 12.D. Y. Chen, M. Ouhyoung, X. P. Tian, Y. T. Shen, and M. Ouhyoung: On visual similarity based 3D model retrieval, Proceedings of Eurographics, Granada, Spain, 2003.Google Scholar
- 13.M. Kazhdan, T. Funkhouser, and S. Rusinkiewicz: Rotation invariant spherical harmonic representation of 3d shape descriptors, Symposium on Geometry Processing, pp. 167–175, 2003.Google Scholar
- 14.T. Saito, S. Banjo, and J. Toriwaki: An improvement of three dimensional thinning method using a skeleton based on the Euclidean distance transformation—A method to control spurious branches, IEICE Transaction on Information and Systems, Japan, J84-D-II, 8, pp. 1628–1635, August 2001.Google Scholar
- 17.F. Wu, W. Ma, P. Liou, R. Liang, and M. Ouhyoung: Skeleton extraction of 3D objects with visible repulsive force, Eurographics Symposium on Geometry Processing, 2003.Google Scholar
- 19.W. Ma, F. Wu, and M. Ouhyoung: Skeleton extraction of 3D objects with radial basis functions, IEEE SMA, 2003.Google Scholar