Abstract
In this paper, a fast automatic segmentation algorithm based on AdaBoost learning and iterative Graph-Cuts are shown. AdaBoost learning method is introduced for automatically finding the approximate location of certain object. Then an iterative Graph-Cuts method is used to model the segmentation problem. We call our algorithm as AdaBoost Aggregation Iterative Graph-Cuts (AAIGC). Compared to previous methods based on Graph-Cuts, our method is automatic. Once certain object is trained, our algorithm can cut it out from an image containing the certain object. The segmentation process is reliably computed automatically no additional users’ efforts are required. Experiments are given and the outputs are encouraging.
This work has been supported by NSFC Project 60573182, 69883004 and 50338030.
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
Reese, L.: Intelligent Paint: Region-Based Interactive Image Segmentation, Master’s thesis. Department of Computer Science, Brigham Young University, Provo, UT (1999)
Mortensen, E.N., Barrett, W.A.: Interactive segmentation with intelligent scissors. Graphical Models and Image Processing 60(5), 349–384 (1998)
Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Proceedings of ICCV, pp. 105–112 (2001)
Rother, C., Blake, A., Kolmogorov, V.: Grabcut-interactive foreground extraction using iterated graph cuts. In: Proceedings of ACM SIGGRAPH (2004)
Comanicu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. (May 2002)
Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 731–737 (1997)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22(8), 888–905 (2000)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. CVPR, vol. 1, pp. 511–518 (2001)
Lienhart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: IEEE ICIP 2002, September 2002, vol. 1, pp. 900–903 (2002)
Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy snapping. In: Proc. of ACM SIGGRAPH, pp. 303–308 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Han, D., Li, W., Lu, X., Wang, Y., Zou, X. (2006). Certain Object Segmentation Based on AdaBoost Learning and Nodes Aggregation Iterative Graph-Cuts. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2006. Lecture Notes in Computer Science, vol 4069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11789239_20
Download citation
DOI: https://doi.org/10.1007/11789239_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36031-5
Online ISBN: 978-3-540-36032-2
eBook Packages: Computer ScienceComputer Science (R0)