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Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering
Zakani F.R., Bouksim M., Arhid K., Aboulfatah M., Gadi T.

Laboratory of Informatics, Imaging, and Modeling of Complex Systems (LIIMSC) Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco,
Laboratory of Analysis of Systems and Treatment of Information (LASTI) Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

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DOI: 10.18287/2412-6179-2018-42-2-312-319

Страницы: 312-319.

Аннотация:
3D mesh segmentation has become an essential step in many applications in 3D shape analysis. In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation. Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the benchmark proposed by Chen et al. to extract the candidate boundaries of a given 3D-model based on a set of geometric criteria. Then, we use this resulted knowledge to construct a new connectivity of the mesh and use the spectral clustering method to segment the 3D mesh into significant parts. Our approach was evaluated using different evaluation metrics. The experiments confirm that the proposed method yields significantly good results and outperforms some of the competitive segmentation methods in the literature.

Ключевые слова:
3D shapes, segmentation, artificial neural networks, spectral clustering.

Цитирование:
Zakani FR, Bouksim M, Arhid K, Aboulfatah M, Gadi T. Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering. Computer Optics 2018; 42(2): 312-319. DOI: 10.18287/2412-6179-2018-42-2-312-319.

Литература:

  1. Attene M, Katz S, Mortara M, Patane G, Spagnuolo M, Tal A. Mesh segmentation – A comparative study. IEEE International Conference on Shape Modeling and Applications 2006 (SMI’06) 2006: 7. DOI: 10.1109/SMI.2006.24.
  2. Shamir A. A survey on mesh segmentation techniques. Computer Graphics Forum 2008; 27(6): 1539-1556. DOI: 10.1111/j.1467-8659.2007.01103.x.
  3. Theologou P, Pratikakis I, Theoharis T. A comprehensive overview of methodologies and performance evaluation frameworks in 3D mesh segmentation. Computer Vision and Image Understanding 2015; 135: 49-82. DOI: 10.1016/j.cviu.2014.12.008.
  4. Chahhou M, Moumoun L, El Far M, Gadi T. Segmentation of 3D meshes using p-spectral clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 2014; 36(8): 1687-1693. DOI: 10.1109/TPAMI.2013.2297314.
  5. Khadija A, Zakani FR, Bouksim M, Aboulfatah M, Gadi T. An efficient hierarchical 3D mesh segmentation using negative curvature and dihedral angle. International Journal of Intelligent Engineering and Systems 2017; 10(5): 143-152. DOI: 10.22266/ijies2017.1031.16.
  6. Benhabiles H, Lavoué G, Vandeborre JP, Daoudi M. Learning boundary edges for 3D-mesh segmentation. Computer Graphics Forum 2011; 30(8): 2170-2182. DOI: 10.1111/j.1467-8659.2011.01967.x.
  7. Yang F, Zhou F, Wang R, Liu L, Luo X. A fast and efficient mesh segmentation method based on improved region growing. Applied Mathematics-A Journal of Chinese Universities 2014; 29(4): 468-480. DOI: 10.1007/s11766-014-3240-0.
  8. Zuckerberger E, Tal A, Shlafman S. Polyhedral surface decomposition with applications. Computers & Graphics 2002; 26(5): 733-743. DOI: 10.1016/S0097-8493(02)00128-0.
  9. Chen L, Georganas ND. An efficient and robust algorithm for 3D mesh segmentation. Multimedia Tools and Applications 2006; 29(2): 109-125. DOI: 10.1007/s11042-006-0002-x.
  10. Shymon Shlafman, Ayellet Tal SK, Shlafman S, Tal A, Katz S. Metamorphosis of polyhedral surfaces using decomposition. Computer Graphics Forum 2002; 21: 219-228. DOI: 10.1111/1467-8659.00581.
  11. Liu R, Zhang H. Segmentation of 3D meshes through spectral clustering. Proceedings of the 12th Pacific Conference on Computer Graphics and Applications 2004; 298-305. DOI: 10.1109/PCCGA.2004.1348360.
  12. Asafi S, Goren A, Cohen-Or D. Weak convex decomposition by lines-of-sight. Computer Graphics Forum 2013; 32(5): 23-31. DOI: 10.1111/cgf.12169.
  13. Hoffman DD, Richards WA. Parts of recognition. Cognition 1984; 18(1-3): 65-96. DOI: 10.1016/0010-0277(84)90022-2.
  14. Theologou P, Pratikakis I, Theoharis T. Unsupervised spectral mesh segmentation driven by heterogeneous graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 2017; 39(2): 397-410. DOI: 10.1109/TPAMI.2016.2544311.
  15. Kalogerakis E, Hertzmann A, Singh K. Learning 3D mesh segmentation and labeling. ACM Transactions on Graphics 2010; 29(4): 102. DOI: 10.1145/1833349.1778839.
  16. Lv J, Chen X, Huang J, Bao H. Semi-supervised mesh segmentation and labeling. Computer Graphics Forum 2012; 31(7): 2241-2248. DOI: 10.1111/j.1467-8659.2012.03217.x.
  17. Lippmann RP. Pattern classification using neural networks. IEEE Communications Magazine 1989; 27(11): 47-50. DOI: 10.1109/35.41401.
  18. Anderson JA, Rosenfeld E, eds. Neurocomputing: Foundations of research. Cambridge: MIT Press; 1988. ISBN: 978-0-26201097-9.
  19. Kohonen T. Neural Modeling. In Book: Kohonen T. Self-organizing maps. Berlin, Heidelberg: Springer-Verlag; 2001; 71-104. DOI: 10.1007/978-3-642-56927-2_2.
  20. Bühler T, Hein M. Spectral Clustering based on the graph p-Laplacian. ICML ’09 Proceedings of the 26th Annual International Conference on Machine Learning 2009; 382: 11-88. DOI: 10.1145/1553374.1553385.
  21. Ng AY, Jordan MI, Weiss Y. On spectral clustering: analysis and an algorithm. Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic 2001: 849-856.
  22. Hagen L, Kahng AB. New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 1992; 11(9): 1074-1085. DOI: 10.1109/43.159993.
  23. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22(8): 888-905. DOI: 10.1109/34.868688.
  24. Amghibech S. Eigenvalues of the discrete p-Laplacian for graphs. Ars Combinatoria 2003; 67: 283-302.
  25. Shapira L, Shamir A, Cohen-Or D. Consistent mesh partitioning and skeletonisation using the shape diameter function. The Visual Computer 2008; 24(4): 249-259. DOI: 10.1007/s00371-007-0197-5.
  26. Koenderink JJ, van Doorn AJ. Surface shape and curvature scales. Image and Vision Computing 1992; 10(8): 557-564. DOI: 10.1016/0262-8856(92)90076-F.
  27. Chen X, Golovinskiy A, Funkhouser T. A benchmark for 3D mesh segmentation. ACM Transactions on Graphics 2009; 28(3): 73. DOI: 10.1145/1531326.1531379.
  28. Liu Z, Tang S, Bu S, Zhang H. New evaluation metrics for mesh segmentation. Computers and Graphics (Pergamon) 2013; 37(6): 553-564. DOI: 10.1016/j.cag.2013.05.021.
  29. Rafii Zakani F, Arhid K, Bouksim M, Aboulfatah M, Gadi T. New measure for objective evaluation of mesh segmentation algorithms. 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt) 2016: 416-421. DOI: 10.1109/CIST.2016.7805083.
  30. Rafii Zakani F, Arhid K, Bouksim M, Gadi T, Aboulfatah M. Kulczynski similarity index for objective evaluation of mesh segmentation algorithms. Proceedings of the 5th International Conference on Multimedia Computing and Systems (ICMCS) 2016: 12-17. DOI: 10.1109/ICMCS.2016.7905611.
  31. Arhid K, Bouksim M, Rafii Zakani F, Gadi T, Aboulfatah M. An objective 3D mesh segmentation evaluation using Sokal-Sneath metric. Proceedings of the 5th International Conference on Multimedia Computing and Systems (ICMCS) 2016: 29-34. DOI: 10.1109/ICMCS.2016.7905609.
  32. Bouksim M, Rafii Zakani F, Arhid K, Aboulfatah M, Gadi T. New evaluation method for 3D mesh segmentation. 4th IEEE International Colloquium on Information Science and Technology (CiSt) 2016: 438-443. DOI: 10.1109/CIST.2016.7805087.
  33. Bouksim M, Zakani FR, Arhid K, Gadi T, Aboulfatah M. Evaluation of 3D mesh segmentation using a weighted version of the Ochiai index. IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) 2016: 1-7. DOI: 10.1109/AICCSA.2016.7945640.
  34. Rafii Zakani F, Arhid K, Bouksim M, Aboulfatah M, Gadi T. A new evaluation method for mesh segmentation based on the Levenshtein distance. International Review on Computers and Software (IRECOS) 2016; 11(12): 1117. DOI: 10.15866/irecos.v11i12.10922.
  35. Golovinskiy A, Funkhouser T. Randomized cuts for 3D mesh analysis. ACM Transactions on Graphics 2008; 27(5): 145. DOI: 10.1145/1409060.1409098.
  36. Attene M, Falcidieno B, Spagnuolo M. Hierarchical mesh segmentation based on fitting primitives. The Visual Computer 2006; 22(3): 181-193. DOI: 10.1007/s00371-006-0375-x.
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