Abstract

This study addresses the problem of 3D shape retrieval. While this problem is interesting and emerging as the size of 3D object databases grows rapidly, the main two issues the community has to focus on are: computational efficiency of 3D object retrieval and the quality of retrieval results. In this study we deal with the two considerations, especially the first one namely computational efficiency, by proposing a new technique to retrieve efficiently the 3D-objects in the classified databases which contains 3D objects of different categories. This technique can be coupled with any 3D retrieval method. In this study, we use the Clock Matching Bag-of-Features 3D retrieval method proposed by Lian et al. (2010) since it gives the best result comparing with several methods in particular the view based methods. Instead of systematically matching the object-query with all 3D objects of the target database, our approach restricts the pattern matching on a subset of “good candidates” (the most similar to the query). For a database classified in several classes the retrieval will be oriented to the right class that contains similar objects to the query. In this case, the matching process will be not systematically performed with all objects among the database, but only with objects of right class. Our key idea is to represent each class by one representative that will be used to orient the retrieval process to the right class. Experimental results illustrate the efficiency of our approach.

Keywords:

3D classified database, 3D content-based shape retrieval, representatives of classes, right class,

References

1. Biasotti, S., D. Giorgi, S. Marini, M. Spagnuolo and B. Falcidieno, 2006. A comparison framework for 3D object classification methods. Proceeding of the International Workshop, MRCS 2006 Lecture Notes in Computer Science Multimedia Content Representation, Classification and Security, 4105: 314-321.
   CrossRef    
2. Lian, Z., G. Afzal and S. Xianfang, 2010. Visual similarity based 3d shape retrieval using bag-of-features. IEEE International Conference on Shape Modeling and applications (SMI) 2010.
   
3. Remco, C.V., J. Geert, B. Hannah, B. Thomas, F. Takahiko, G. Joachim, G. Afzal, L. Zhouhui, O. Ryutarou and S. Waqar, 2010. SHREC'10 Track: Large Scale Retrieval. Proceedings of the 3rd Eurographics Conference on 3D Object Retrieal, pp: 63-69.
   
4. Roberto, T., C. Umberto and F. Andrea, 2009. A bag of words approach for 3d object categorization. Proceedings of the Lecture Notes in Computer Science 5496 Springer, Berlin Heidelberg, pp: 116-127.
   
5. Shilane, P., P. Min, M. Kazhdan and T. Funkhouser, 2004.The Princeton shape benchmark. Proceeding of the IEEE Shape Modeling and Applications Conference, SMI' 2004, Genova, Italy, pp: 167-178.
   CrossRef    
6. Tangelder, J.W.H. and R.C. Veltkamp, 2008. A survey of content based 3D shape retrieval methods. Multimed. Tools Appl., 39(3): 441-471.
   CrossRef    
7. Zaharia, T. and F. Preteux, 2004. 3D versus 2D/3D shape descriptors: A comparative study. In: Proc. SPIE Conf. on Image process: Algorithms and Syst. III- SPIE Sym Electron. Imaging, Sci. Technol., 5298: 47-58.
   

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.