Abstract
An automatic segmentation algorithm based on AdaBoost learning and iterative Graph-Cuts are shown in this paper. In order to find the approximate location of the object, AdaBoost learning method is used to automatically find the object by the trained classifier. Some details on AdaBoost are described. Then the nodes aggregation method and the iterative Graph-Cuts method are used to model the automatic segmentation problem. Compared to previous methods based on Graph-Cuts, our method is automatic. This is a main feature of the proposed algorithm. Experiments and comparisons show the efficiency of the proposed method.
This work has been supported by NSFC Project 60573182, 69883004 and 50338030.
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© 2006 Springer-Verlag Berlin Heidelberg
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Han, D., Li, W., Lu, X., Wang, T., Wang, Y. (2006). Automatic Segmentation Based on AdaBoost Learning and Graph-Cuts. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_21
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DOI: https://doi.org/10.1007/11867586_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44891-4
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