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Selecting Representative Views of 3D Objects By Affinity Propagation for Retrieval and Classification

검색과 분류를 위한 친근도 전파 기반 3차원 모델의 특징적 시점 추출 기법

  • Lee, Soo-Chahn (School of Electrical Engineering and Computer Science, Seoul National University) ;
  • Park, Sang-Hyun (School of Electrical Engineering and Computer Science, Seoul National University) ;
  • Yun, Il-Dong (School of Digital Information Engineering, Hankuk University of Foreign Studies) ;
  • Lee, Sang-Uk (School of Electrical Engineering and Computer Science, Seoul National University)
  • 이수찬 (서울대학교 전기.컴퓨터공학부) ;
  • 박상현 (서울대학교 전기.컴퓨터공학부) ;
  • 윤일동 (한국외국어대학교 용인캠퍼스 디지털정보공학과) ;
  • 이상욱 (서울대학교 전기.컴퓨터공학부)
  • Published : 2008.11.30

Abstract

We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

본 논문은 단일 3차원 모델과 모델의 클래스의 특징적인 시점을 추출하여 3차원 모델 검색 및 분류를 수행하는 기법을 제안한다. 제안하는 기법은 3차원 모델을 투영한 2차원 형상 중에 특징적인 형상을 추출하는데, 이때 고르게 샘플(sample)된 형상들을 최근 개발된 친근도 전파 (affinity propagation) 기법을 이용하여 군집화(clustering)한다. 친근도 전파는 데이터를 군집화하는 동시에 각 클러스터의 대표 값을 계산하므로, 군집화된 형상들로부터 대표 형상이 자연스럽게 지정된다. 제안하는 기법은 친근도 기법을 클래스별로 각 모델의 대표 형상 집합에 재차 적용하여 클래스의 대표 형상을 추출하고, 이를 기반으로 하여 3차원 모델의 분류도 가능하게 한다. 3차원 모델의 검색 뿐 아니라 분류를 가능하게 함으로써, 분류를 검색의 전처리 과정으로 하여 연관된 클래스의 모델 중에서만 검색을 수행할 수 있게 하여 단위가 큰 데이터베이스에서도 효율적인 검색을 가능하게 한다. [16]에 제안된 프린스턴 벤치마크 데이터베이스(Princeton benchmark database)을 이용한 실험을 통해 제안하는 검색 및 분류 기법의 유용함을 보인다.

Keywords

References

  1. F.J. Brendan and D. Dueck. Clustering by passing messages be tween data points. Science, 315:972-976, February 2007 https://doi.org/10.1126/science.1136800
  2. R.J. Campbell and P.J. Flynn. A survey of free-form object representation and recognition techniques. Computer Vision and Image Understanding, 81(2):166-210, 2001 https://doi.org/10.1006/cviu.2000.0889
  3. D.-Y. Chen, M. Ouhyoung, X.-P. Tian, Y.-T. Shen, and M. Ouhyoung. On visual similarity based 3d model retrieval. In Eurographics, pages 223-232, 2003
  4. C.M. Cyr and B.B. Kimia. A similarity-based aspect-graph approach to 3d object recognition. International Journal of Computer Vision, 57(1):5-22, 2004 https://doi.org/10.1023/B:VISI.0000013088.59081.4c
  5. R.R. Donamukkala, D. Huber, A. Kapuria, and M. Hebert. Automatic class selection and prototyping for 3-d object classification. In Proceedings of International Conference on 3-D Digital Imaging and Modeling, pages 64-71, June 2005
  6. T. Funkhouser, P. Min, M. Kazhdan, J. Chen, A. Halderman, D. Dobkin, and D. Jacobs. A search engine for 3D models. ACM Transactions on Graphics, 22(1):83-105, January 2003 https://doi.org/10.1145/588272.588279
  7. D. Huber, A. Kapuria, R.R. Donamukkala, and M. Hebert. Parts-based 3d object classification. In Proceedings of Computer Vision and Pattern Recognition, June 2004
  8. M. Kazhdan, B. Chazelle, D. Dobkin, T. Funkhouser, and S. Rusinkiewicz. A reflective symmetry descriptor for 3D models. Algorithmica, 38(1), October 2003
  9. M. Kazhdan, T. Funkhouser, and S. Rusinkiewicz. Rotation invariant spherical harmonic representation of 3D shape descriptors. In Symposium on Geometry Processing, June 2003
  10. S. Lee, S. Yoon, I.D. Yun, D.H. Kim, K.M. Lee, and S.U. Lee. A new 3-d model retrieval system based on spect-transition descriptor. In Proceedings of European Conference on Computer Vision, volume 4, pages 543-554, 2006
  11. B. Manjunath, P. Salembier, and T Sikora. Introduction to MPEG-7. Wiley, 2002
  12. F. Mokhtarian and A.K. Mackworth. A theory of multiscale, curvature-based shape representation for planar curves. IEEE Transcations on Pattern Analsis and Machine Intelligence, 14(8):789-805, 1992 https://doi.org/10.1109/34.149591
  13. R. Ohbuchi, T. Minamitani, and T. Takei. Shape-similarity search of 3d models by using enhanced shape functions. In International Journal of Computer Applications in Technology, volume 23, pages 70-85, 2005 https://doi.org/10.1504/IJCAT.2005.006466
  14. R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. Shape distributions. ACM Transactions on Graphics, 21(4):807-832, October 2002 https://doi.org/10.1145/571647.571648
  15. J. Pearl. Probabilistic Reasoning in Intelligent Systems : Networks of Plausible Inference. Morgan Kaufmann, 1988
  16. Philip Shilane, Patrick Min, Michael Kazhdan, and Thomas Funkhouser. The princeton shape benchmark. In Shape Modeling International, June 2004 https://doi.org/10.1109/SMI.2004.63
  17. J.W.H. Tangelder and R.C. Veltkamp. A survey of content based 3d shape retrieval methods. Proceedings of Shape Modeling Applications, pages 145-156, 2004
  18. D.V. Vranic. An improvement of rotation invariant 3d-shape based on functions on concentric spheres. In International Conference on Image Processing, volume 3, pages 757-760, 2003
  19. J. Xiao, J. Wang, P. Tan, and L. Quan. Joint affinity propagation for multiple view segmentation. In Proceedings of International Conference on Computer Vision, 2007
  20. Y. Yubin, L. Hui, and Z. Yao. Content-based 3-d model retrieval: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(6):1081-1098, Nov. 2007 https://doi.org/10.1109/TSMCC.2007.905756