Robust Region Detection via Consensus Segmentation of Deformable Shapes
We consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intui...
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| Veröffentlicht in: | Computer graphics forum Jg. 33; H. 5; S. 97 - 106 |
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01.08.2014
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | We consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble, can reveal components in the shape that are more stable to deformations than the single baseline segmentations. Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non‐rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis. |
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| AbstractList | We consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble, can reveal components in the shape that are more stable to deformations than the single baseline segmentations. Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non-rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis. [PUBLICATION ABSTRACT] We consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble, can reveal components in the shape that are more stable to deformations than the single baseline segmentations. Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non‐rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis. |
| Author | Cremers, D. Bulò, S. Rota Rodolà, E. |
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| Cites_doi | 10.1007/978-3-642-23123-0_13 10.1145/1409060.1409098 10.1007/s00371-013-0870-9 10.1016/j.cag.2011.03.011 10.1007/978-3-662-05105-4_2 10.1007/s11263-005-3848-x 10.1111/j.1467-8659.2011.02060.x 10.1007/s11263-009-0278-1 10.1109/CVPR.2010.5539838 10.1145/2185520.2185526 10.1016/j.cag.2012.03.034 10.1145/1015706.1015774 10.1109/CVPR.2014.532 10.1111/j.1467-8659.2009.01517.x 10.1109/TPAMI.2011.280 10.1007/978-3-642-18245-7 10.1007/978-3-642-38628-2_8 10.1137/1.9781611973068.112 10.1109/TPAMI.2005.113 10.1111/cgf.12066 10.1162/153244303321897735 |
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| References_xml | – reference: Mikolajczyk K., Tuytelaars T., Schmid C., Zisserman A., et al.: A comparison of affine region detectors. Int. J. Comput. Vis. 65, 1-2 (2005), 43-72. 1, 6 – reference: Litman R., Bronstein A.M., Bronstein M.M.: Stable volumetric features in deformable shapes. Computers and Graphics 36, 5 (2012), 569-576. 1, 2, 5 – reference: Sipiran I., Bustos B.: Key-components: detection of salient regions on 3d meshes. The Visual Computer 29, 12 (2013), 1319-1332. 2, 8 – reference: Bronstein A., Bronstein M., Kimmel R.: Numerical Geometry of Non-Rigid Shapes, 1 ed. Springer Publishing Company, Incorporated, 2008. 6 – reference: Strehl A., Ghosh J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J. of Machine Learning Research, 3 (2002), 583-617. 2 – reference: Litman R., Bronstein A.M., Bronstein M.M.: Diffusion-geometric maximally stable component detection in deformable shapes. Computers and Graphics 35, 3 (2011), 549-560. 2, 4, 5, 6, 7, 8, 9 – reference: Ovsjanikov M., Ben-Chen M., Solomon J., Butscher A., Guibas L.: Functional maps: a flexible representation of maps between shapes. ACM Trans. Graph. 31, 4 (2012), 30:1-30:11. 1, 7, 8 – reference: Pokrass J., Bronstein A.M., Bronstein M.M., SPRECHMANN P., Sapiro G.: Sparse modeling of intrinsic correspondences. Computer Graphics Forum 32, 2pt4 (2013), 459-468. 1, 8 – reference: Reuter M.: Hierarchical shape segmentation and registration via topological features of laplace-beltrami eigenfunctions. Int. J. Comput. Vis. 89, 2-3 (2010), 287-308. 2 – reference: Yu Y., Zhou K., Xu D., Shi X., Bao H., Guo B., Shum H.-Y.: Mesh editing with poisson-based gradient field manipulation. ACM Trans. Graph. 23, 3 (2004), 644-651. 3 – reference: Berger M.: A panoramic view of Riemannian geometry. Springer, Berlin, New York, Heidelberg, 2003. 3 – reference: Alush A., Goldberger J.: Ensemble segmentation using efficient integer linear programming. IEEE Trans. Pattern Analysis and Machine Intelligence 34, 10 (2012), 1966-1977. 2 – reference: Benjamin W., Polk A.W., Vishwanathan S., Ramani K.: Heat walk: Robust salient segmentation of nonrigid shapes. Computer Graphics Forum 30, 7 (2011), 2097- 2106. 2 – reference: Golovinskiy A., Funkhouser T.: Randomized cuts for 3D mesh analysis. ACM Trans. Graph. 27, 5 (2008). 2, 5, 7 – reference: Fred A., Jain A.: Combining multiple clustering using evidence accumulation. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 6 (2005), 835-850. 2 – year: 2011 – volume: 65 start-page: 43 issue: 1 year: 2005 end-page: 2 72 article-title: A comparison of affine region detectors publication-title: Int. J. Comput. Vis – volume: 35 start-page: 549 issue: 3 year: 2011 end-page: 560 article-title: Diffusion‐geometric maximally stable component detection in deformable shapes publication-title: Computers and Graphics – year: 2003 – volume: 27 start-page: 835 issue: 6 year: 2005 end-page: 850 article-title: Combining multiple clustering using evidence accumulation publication-title: IEEE Trans. Pattern Analysis and Machine Intelligence – volume: 27 issue: 5 year: 2008 article-title: Randomized cuts for 3D mesh analysis publication-title: ACM Trans. Graph – volume: 30 start-page: 2097 issue: 7 year: 2011 article-title: Heat walk: Robust salient segmentation of nonrigid shapes publication-title: Computer Graphics Forum – year: 2014 – volume: 29 start-page: 1319 issue: 12 year: 2013 end-page: 1332 article-title: Key‐components: detection of salient regions on 3d meshes publication-title: The Visual Computer – start-page: 1405 year: 2009 end-page: 1413 – year: 2010 – volume: 34 start-page: 1966 issue: 10 year: 2012 end-page: 1977 article-title: Ensemble segmentation using efficient integer linear programming publication-title: IEEE Trans. Pattern Analysis and Machine Intelligence – volume: 3 start-page: 583 year: 2002 end-page: 617 article-title: Cluster ensembles – a knowledge reuse framework for combining multiple partitions publication-title: J. of Machine Learning Research – volume: 32 start-page: 459 year: 2013 end-page: 468 article-title: Sparse modeling of intrinsic correspondences publication-title: Computer Graphics Forum – volume: 23 start-page: 644 issue: 3 year: 2004 end-page: 651 article-title: Mesh editing with poisson‐based gradient field manipulation publication-title: ACM Trans. Graph – volume: 31 start-page: 30:1 issue: 4 year: 2012 end-page: 30:11 article-title: Functional maps: a flexible representation of maps between shapes publication-title: ACM Trans. Graph – start-page: 1031 year: 2009 end-page: 1040 – volume: 36 start-page: 569 issue: 5 year: 2012 end-page: 576 article-title: Stable volumetric features in deformable shapes publication-title: Computers and Graphics – start-page: 1704 year: 2010 end-page: 1711 – year: 2002 – year: 2008 – start-page: 79 year: 2010 end-page: 86 – start-page: 69 year: 2013 end-page: 78 – start-page: 225 year: 2007 end-page: 233 – volume: 89 start-page: 287 issue: 2 year: 2010 end-page: 3 308 article-title: Hierarchical shape segmentation and registration via topological features of laplace‐beltrami eigenfunctions publication-title: Int. J. Comput. 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| SubjectTerms | Analysis Categories and Subject Descriptors (according to ACM CCS) Computational efficiency Computer graphics Deformation Formability I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Shape Analysis Image processing systems Robustness Segmentation Statistics Studies Three dimensional models Topological manifolds Transformations |
| Title | Robust Region Detection via Consensus Segmentation of Deformable Shapes |
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