Authors:
Ahmed Al-Taie
1
;
Horst K. Hahn
2
and
Lars Linsen
3
Affiliations:
1
Jacobs University, College of Science for Women and Baghdad University, Germany
;
2
Jacobs University and Fraunhofer MEVIS, Germany
;
3
Jacobs University, Germany
Keyword(s):
Ensemble of Classifiers, Image Segmentation, Diversity.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Medical Image Applications
;
Segmentation and Grouping
;
Visual Attention and Image Saliency
Abstract:
The idea of using ensembles of classifiers is to increase the performance when compared to applying a single
classifier. Crucial to the performance improvement is the diversity of the ensemble. A classifier ensemble
is considered to be diverse, if the classifiers make no coinciding errors. Several studies discuss the diversity
issue and its relation to the ensemble accuracy. Most of them proposed measures that are based on an ”Oracle”
classification. In this paper, we propose a new probability-based diversity measure for ensembles of unsupervised
classifiers, i.e., when no Oracle machine exists. Our measure uses a point-wise definition of diversity,
which allows for a distinction of diverse and non-diverse areas. Moreover, we introduce the concept of further
categorizing the diverse areas into healthy and unhealthy diversity areas. A diversity area is healthy for the
ensemble performance, if there is enough redundancy to compensate for the errors. Then, the performance
of the ense
mble can be based on two parameters, the non-diversity area, i.e., the size of all regions where the
classifiers of the ensemble agree, and the healthy diversity area, i.e., the size of the regions where the diversity
is healthy. Furthermore, our point-wise diversity measure allows for an intuitive visualization of the ensemble
diversity for visual ensemble performance comparison in the context of image segmentation.
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