Abstract
We propose a combination of multiple Conditional Random Field (CRF) models with a linear classifier. The model is used for the semantic labeling of 3-D surface meshes with large variability in shape. The model employs multiple CRFs of low complexity for surface labeling each of which models the distribution of labelings for a group of surfaces with a similar shape. Given a test surface the classifier exploits the MAP energies of the inferred CRF labelings to determine the shape class. We discuss the associated recognition and learning tasks and demonstrate the capability of the joint shape classification and labeling model on the object category of human outer ears.
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References
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE PAMI 24(24) (2002)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004)
Boykov, Y., et al.: Efficient approximate energy minimization via graph cuts. PAMI (2001)
Chen, X., Golovinskiy, A., Funkhouser, T.: A benchmark for 3D mesh segmentation. ACM Transactions on Graphics (Proc. SIGGRAPH) 28(3) (August 2009)
Cipriano, G., Phillips Jr., G.N., Gleicher, M.: Multiscale surface descriptors. IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization 2009) (October 2009)
Flach, B., Schlesinger, D.: Combining shape priors and MRF-segmentation. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) S+SSPR 2008. LNCS, vol. 5342, pp. 177–186. Springer, Heidelberg (2008)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Golovinskiy, A., Funkhouser, T.: Randomized cuts for 3D mesh analysis. ACM Transactions on Graphics (Proceedings SIGGRAPH Asia) 27 (2008)
Golovinskiy, A., Funkhouser, T.: Consistent segmentation of 3D models. Computers and Graphics (Shape Modeling International 09) 33(3), 262–269 (2009)
Johnson, A.E., Hebert, M.: Using spin-images for efficient multiple model recognition in cluttered 3-D scenes. IEEE PAMI 21(5), 433–449 (1999)
Kalogerakis, E., Hertzmann, A., Singh, K.: Learning 3D mesh segmentation and labeling. SIGGRAPH 2010 (2010)
Koertgen, M., Park, G.J., Novotni, M., Klein, R.: 3D shape matching with 3d shape contexts. In: Proceedings of The 7th Central European Seminar on Computer Graphics (2003)
Kumar, M.P., Torr, P.H.S., Zisserman, A.: Obj cut. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, vol. 1, pp. 18–25 (2005)
Lafferty, J.D., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. ICML (2001)
Nowozin, S., Lampert, C.H.: Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision 6(3-4), 185–365 (2011)
Shapira, L., Shamir, A., Cohen-Or, D.: Consistent mesh partitioning and skeletonisation using the shape diameter function. Visual Computing 24, 249–259 (2008)
Shi, Y., et al.: Direct mapping of hippocampal surfaces with intrinsic shape context. Neuroimage (2007)
Slabaugh, G., Fang, T., McBagonluri, F., Zouhar, A., Melkisetoglu, R., Xie, H., Unal, G.: 3-D Shape Modeling for Hearing Aid Design. IEEE Signal Processing Magazine (2008)
Winn, J., Shotton, J.: The layout consistent random field for recognizing and segmenting partially occluded objects. In: CVPR (2006)
Zouhar, A., Baloch, S., Tsin, Y., Fang, T., Fuchs, S.: Layout Consistent Segmentation of 3-D meshes via Conditional Random Fields and Spatial Ordering Constraints. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 113–120. Springer, Heidelberg (2010)
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Zouhar, A., Schlesinger, D., Fuchs, S. (2013). Joint Shape Classification and Labeling of 3-D Objects Using the Energy Minimization Framework. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_8
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DOI: https://doi.org/10.1007/978-3-642-40602-7_8
Publisher Name: Springer, Berlin, Heidelberg
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