Automatic segmentation based on adaboost learning and graph-cuts

Authors:
Dongfeng Han;Wenhui Li;Xiaosuo Lu;Tianzhu Wang;Yi Wang
Affiliations:
College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education,Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education,Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education,Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education,Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education,Jilin University, Changchun, P.R. China
Venue:
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Year:
2006

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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.