loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

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

ISBN: 978-989-758-089-5

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 ensem ble 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. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.175.120.174

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Al-Taie, A.; Hahn, H. and Linsen, L. (2015). Point-wise Diversity Measure and Visualization for Ensemble of Classifiers - With Application to Image Segmentation.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 569-576. DOI: 10.5220/0005309605690576

@conference{visapp15,
author={Ahmed Al{-}Taie. and Horst K. Hahn. and Lars Linsen.},
title={Point-wise Diversity Measure and Visualization for Ensemble of Classifiers - With Application to Image Segmentation},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={569-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005309605690576},
isbn={978-989-758-089-5},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Point-wise Diversity Measure and Visualization for Ensemble of Classifiers - With Application to Image Segmentation
SN - 978-989-758-089-5
AU - Al-Taie, A.
AU - Hahn, H.
AU - Linsen, L.
PY - 2015
SP - 569
EP - 576
DO - 10.5220/0005309605690576

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.