A clustering-based ensemble technique for shape decomposition

Authors:
Sergej Lewin;Xiaoyi Jiang;Achim Clausing
Affiliations:
Department of Mathematics and Computer Science, University of Münster, Münster, Germany;Department of Mathematics and Computer Science, University of Münster, Münster, Germany;Department of Mathematics and Computer Science, University of Münster, Münster, Germany
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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Abstract

Ensemble techniques have been very successful in pattern recognition. In this work we investigate ensemble solution for shape decomposition. A clustering-based approach is proposed to determine a final decomposition from an ensemble of input decompositions. A recently published performance evaluation framework consisting of a benchmark database with manual ground truth together with evaluation measures is used to demonstrate the benefit of the proposed ensemble technique.