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.
Keywords
- Dissimilarity Measure
- Ensemble Technique
- Benchmark Database
- Majority Vote Rule
- Majority Vote Scheme
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Lewin, S., Jiang, X., Clausing, A. (2012). A Clustering-Based Ensemble Technique for Shape Decomposition. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_17
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DOI: https://doi.org/10.1007/978-3-642-34166-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34165-6
Online ISBN: 978-3-642-34166-3
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