Abstract
In this paper, an object segmentation algorithm based on automatic shape constraint selection is proposed. Different from the traditional shape prior based object segmentation methods which only provide loose shape constraints, our proposed object segmentation gives more accurate shape constraint by selecting the most appropriate shape among the standard shape set. Furthermore, to overcome the inevitable differences between the true borders and the standard shapes, the Coherent Point Drift (CPD) is adopted to project the standard shapes to the local ones. A quantitative evaluating mechanism is introduced to pick out the most suitable shape prior. The proposed algorithm mainly consists of four steps: 1) the initial GrabCut segmentation; 2) standard shape projection by CPD registration; 3) rank the standard shapes according to the evaluation scores; 4) refine GrabCut segmentation with the chosen shape constraint. The comparison experiments with the related algorithms on Weizmann_horse dataset have demonstrated the good performance of the proposed algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: Proc. Int. Conf. Computer Vision, vol. 1, pp. 105–112 (2001)
Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph 23, 309–314 (2004)
Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy snapping. In: Proc. SIGGRAPH Conf., pp. 303–308 (2004)
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)
Slabaugh, G., Unal, G.: Graph cuts segmentation using an elliptical shape prior. In: IEEE International Conference on Image Processing, vol. 2, pp. II-1222–II-1225 (2005)
Funka-Lea, G., Boykov, Y., Florin, C., Jolly, M.P., Moreau-Gobard, R., Ramaraj, R., Rinck, D.: Automatic heart isolation for ct coronary visualization using graph-cuts. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 614–617 (2006)
Das, P., Veksler, O.: Semiautomatic segmentation with compact shapre prior. In: The 3rd Canadian Conference on Computer and Robot Vision, pp. 28–28 (2006)
Vicente, S., Kolmogorov, V., Rother, C.: Graph cut based image segmentation with connectivity priors. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Veksler, O.: Star shape prior for graph-cut image segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 454–467. Springer, Heidelberg (2008)
Gulshan, V., Rother, C., Criminisi, A., Blake, A., Zisserman, A.: Geodesic star convexity for interactive image segmentation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Francisco, CA, USA, pp. 3129–3136 (2010)
Freedman, D., Zhang, T.: Interactive graph cut based segmentation with shape priors. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Diego, CA, USA, vol. 1, pp. 755–762 (2005)
Kim, J., Grauman, K.: Shape sharing for object segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 444–458. Springer, Heidelberg (2012)
Myronenko, A., Song, X.: Point set registration: Coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(12), 2262–2275 (2010)
Dempster, A.P., Laird, N.M., Rubin, D.B., et al.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society 39(1), 1–38 (1977)
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
Li, K., Tao, W., Liao, X., Liu, L. (2015). Automatic Shape Constraint Selection Based Object Segmentation. In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, Cham. https://doi.org/10.1007/978-3-319-14612-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-14612-6_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14611-9
Online ISBN: 978-3-319-14612-6
eBook Packages: Computer ScienceComputer Science (R0)