Abstract
This paper presents a novel online 3D shape segmentation framework, which blend two learning methods together: unsupervised clustering based method, and supervised progressive learning method. The features of this method lie in four aspects. Firstly, we use weighted online learning to train a segmentation model to achieve the blended learning framework. Secondly, we perform co-segmentation based on unsupervised clustering to analyze the shape set, and initialize this segmentation model. Thirdly, based on this segmentation model, users can segment new shapes by using supervised progressive learning method. And this segmentation model can also be incrementally updated by weighted online learning during the progressive segmentation. Finally, the segmentation of shapes in the initial set can be corrected based on the updated segmentation model. Experimental results demonstrate the effectiveness of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schröder, P., Sweldens, W.: Digital geometry processing. In: Frontiers of Engineering: Reports on Leading-Edge Engineering From the 2000 NAE Symposium on Frontiers in Engineering, p. 41. National Academies Press (2001)
Chen, X., Golovinskiy, A., Funkhouser, T.: A benchmark for 3d mesh segmentation. ACM Trans. Graph. 28(3), 73:1–73:12 (2009)
Golovinskiy, A., Funkhouser, T.: Consistent segmentation of 3d models. Computers and Graphics 33(3), 262–269 (2009)
Sidi, O., van Kaick, O., Kleiman, Y., Zhang, H., Cohen-Or, D.: Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering. ACM Trans. Graph. 30(6), 126:1–126:10 (2011)
Huang, Q., Koltun, V., Guibas, L.: Joint shape segmentation with linear programming. ACM Trans. Graph. 30 (6), 125:1–125:12. (2011)
Hu, R., Fan, L., Liu, L.: Co-segmentation of 3d shapes via subspace clustering. Computer Graphics Forum 31(5), 1703–1713 (2012)
Wang, Y., Asafi, S., van Kaick, O., Zhang, H., Cohen-Or, D., Chen, B.: Active co-analysis of a set of shapes. ACM Trans. Graph. 31(6), 165 (2012)
Meng, M., Xia, J., Luo, J., He, Y.: Unsupervised co-segmentation for 3d shapes using iterative multi-label optimization. Comput. Aided Des. 45(2), 312–320 (2013)
Luo, P., Wu, Z., Xia, C., Feng, L., Ma, T.: Co-segmentation of 3d shapes via multi-view spectral clustering. The Visual Computer 29(6-8), 587–597 (2013)
Wu, Z., Wang, Y., Shou, R., Chen, B., Liu, X.: Unsupervised co-segmentation of 3d shapes via affinity aggregation spectral clustering. Computers and Graphics 37(6), 628–637 (2013)
Zhang, F.Q., Sun, Z.X., Song, M.F., Lang, X.F., Yan, H.: 3d shapes co-segmentation by combining fuzzy c-means with random walks. In: Proceedings of 13th International Conference on Computer-Aided Design and Computer Graphics, pp. 16–23. IEEE Press, Hong Kong (2013)
Kim, V.G., Li, W., Mitra, N.J., Chaudhuri, S., DiVerdi, S., Funkhouser, T.: Learning part-based templates from large collections of 3d shapes. ACM Trans. Graph. 32(4), 70:1–70:12 (2013)
Lv, J., Chen, X., Huang, J., Bao, H.: Semi-supervised mesh segmentation and labeling. Computer Graphics Forum 31(7), 2241–2248 (2012)
Wu, Z., Shou, R., Wang, Y., Liu, X.: Interactive shape co-segmentation via label propagation. Computers and Graphics 38, 248–254 (2014)
Zhang, F.Q., Sun, Z.X., Song, M.F., Lang, X.F.: Progressive 3D shape segmentation using online learning. Computer-Aided Design 58, 2–12 (2015)
Kalogerakis, E., Hertzmann, A., Singh, K.: Learning 3d mesh segmentation and labeling. ACM Trans. Graph. 29(4), 102:1–102:12 (2010)
Gal, R., Cohen-Or, D.: Salient geometric features for partial shape matching and similarity. ACM Trans. Graph. 25(1), 130–150 (2006)
Shapira, L., Shalom, S., Shamir, A., Cohen-Or, D., Zhang, H.: Contextual part analogies in 3d objects. Int. J. Comput. Vision. 89(2-3), 309–326 (2010)
Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3d shapes. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2001, pp. 203–212. ACM Press, New York (2001)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: 2009 IEEE 12th International Conference on Computer VisionWorkshops (ICCV Workshops), pp. 1393–1400. IEEE Press, Kyoto (2009)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, F., Sun, Z., Song, M., Lang, X. (2015). Online 3D Shape Segmentation by Blended Learning. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-14445-0_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14444-3
Online ISBN: 978-3-319-14445-0
eBook Packages: Computer ScienceComputer Science (R0)