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. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Lewin, S., Jiang, X., & Clausing, A. (2012). A clustering-based ensemble technique for shape decomposition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7626 LNCS, pp. 153–161). https://doi.org/10.1007/978-3-642-34166-3_17
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