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Authors: Junji Otsuka and Tomoharu Nagao

Affiliation: Yokohama National University, Japan

ISBN: 978-989-758-015-4

Keyword(s): Fuzzy Inference, Genetic Algorithm, Image Segmentation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Evolutionary Computing ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: This paper presents Cellular Fuzzy Oriented Classifier Evolution (CFORCE), a generic method for constructing fuzzy rules to divide an image into two segments: object and background. In CFORCE, a pair of fuzzy classification rule sets for object and background is defined as a processing unit, and the identical units are allocated on each pixel over an input image. Each unit computes matching degree of each pixel with object and background class iteratively with considering the matching degree of neighbor units. The algorithm has mainly two features: 1) designing the fuzzy rules using Fuzzy Oriented Classifier Evolution (FORCE) which develops fuzzy rules represented as directed graphs flexibly and automatically by Genetic Algorithm, and 2) performing iterative segmentation with considering spatial relationship between pixels besides local features. In natural image segmentation, many pixels are overlapped between different clusters. Considering the spatial relationship is important to c lassify the overlapped pixels correctly. We applied CFORCE to three different object segmentation, and showed that CFORCE extracted object regions successfully. (More)

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Paper citation in several formats:
Otsuka, J. and Nagao, T. (2014). Evolutionary Fuzzy Rule Construction for Iterative Object Segmentation.In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 84-93. DOI: 10.5220/0004801500840093

@conference{icaart14,
author={Junji Otsuka. and Tomoharu Nagao.},
title={Evolutionary Fuzzy Rule Construction for Iterative Object Segmentation},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={84-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004801500840093},
isbn={978-989-758-015-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Evolutionary Fuzzy Rule Construction for Iterative Object Segmentation
SN - 978-989-758-015-4
AU - Otsuka, J.
AU - Nagao, T.
PY - 2014
SP - 84
EP - 93
DO - 10.5220/0004801500840093

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