Pattern Recognition and Image Analysis

, Volume 27, Issue 3, pp 444–457 | Cite as

Combining rules using local statistics and uncertainty estimates for improved ensemble segmentation

Representation, Processing, Analysis, and Understanding of Images
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Abstract

Segmentation using an ensemble of classifiers (or committee machine) combines multiple classifiers’ results to increase the performance when compared to single classifiers. In this paper, we propose new concepts for combining rules. They are based (1) on uncertainties of the individual classifiers, (2) on combining the result of existing combining rules, (3) on combining local class probabilities with the existing segmentation probabilities at each individual segmentation, and (4) on using uncertainty-based weights for the weighted majority rule. The results show that the proposed local-statistics-aware combining rules can reduce the effect of noise in the individual segmentation result and consequently improve the performance of the final (combined) segmentation. Also, combining existing combining rules and using the proposed uncertainty- based weights can further improve the performance.

Keywords

ensemble of classifiers combining rules image segmentation modified Fuzzy c-means 

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Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  1. 1.Jacobs UniversityBremenGermany
  2. 2.Fraunhofer MEVISBremenGermany
  3. 3.Westfälischen Wilhelms-UniversitätMünsterGermany

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