Generalized Hard Constraints for Graph Segmentation
Graph-based methods have become well-established tools for image segmentation. Viewing the image as a weighted graph, these methods seek to extract a graph cut that best matches the image content. Many of these methods are interactive, in that they allow a human operator to guide the segmentation process by specifying a set of hard constraints that the cut must satisfy. Typically, these constraints are given in one of two forms: regional constraints (a set of vertices that must be separated by the cut) or boundary constraints (a set of edges that must be included in the cut). Here, we propose a new type of hard constraints, that includes both regional constraints and boundary constraints as special cases. We also present an efficient method for computing cuts that satisfy a set of generalized constraints, while globally minimizing a graph cut measure.
KeywordsImage segmentation Graph cuts Regional constraints Boundary constraints
- 1.Audigier, R., Lotufo, R.A.: Seed-relative segmentation robustness of watershed and fuzzy connectedness approaches. In: Falcão, A.X., Lopes, H. (eds.) Proceedings of the 20th Brazilian Symposium on Computer Graphics and Image Processing, pp. 61–68. IEEE Computer Society, Los Alamitos (2007)Google Scholar
- 2.Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 105–112 (2001)Google Scholar
- 3.Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watersheds: A unifying graph-based optimization framework. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(PrePrints) (2010), doi:10.1109/TPAMI.2010.200.Google Scholar
- 8.Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2) (2004)Google Scholar
- 11.Malmberg, F., Lindblad, J., Sladoje, N., Nyström, I.: A graph-based framework for sub-pixel image segmentation. Theoretical Computer Science (2010), doi:10.1016/j.tcs.2010.11.030.Google Scholar