A cellular coevolutionary algorithm for image segmentation

Clustering is inherently a difficult problem, both with respect to the definition of adequate models as well as to the optimization of the models. We present a model for the cluster problem that does not need knowledge about the number of clusters a priori . This property is among others useful in t...

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Veröffentlicht in:IEEE transactions on image processing Jg. 12; H. 3; S. 304 - 316
Hauptverfasser: Veenman, C.J., Reinders, M.J.T., Backer, E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.03.2003
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applied sciences
Cellular
Clustering algorithms
Clustering methods
Clusters
Exact sciences and technology
Genetic algorithms
Image processing
Image segmentation
Information technology
Information, signal and communications theory
Mathematical models
Optimization
Parameter estimation
Pattern recognition
Pixel
Scattering
Signal processing
Studies
Telecommunications and information theory
Two dimensional displays
Unions
Performance evaluation
Image segmentation
Automatic classification
distributed genetic algorithms
modeling
Image processing
Genetic algorithm
Distributed algorithm
Markov model
Clustering
Optimization
ISSN:1057-7149, 1941-0042
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Beschreibung
Zusammenfassung:Clustering is inherently a difficult problem, both with respect to the definition of adequate models as well as to the optimization of the models. We present a model for the cluster problem that does not need knowledge about the number of clusters a priori . This property is among others useful in the image segmentation domain, which we especially address. Further, we propose a cellular coevolutionary algorithm for the optimization of the model. Within this scheme multiple agents are placed in a regular two-dimensional (2-D) grid representing the image, which imposes neighboring relations on them. The agents cooperatively consider pixel migration from one agent to the other in order to improve the homogeneity of the ensemble of the image regions they represent. If the union of the regions of neighboring agents is homogeneous then the agents form alliances. On the other hand, if an agent discovers a deviant subject, it isolates the subject. In the experiments we show the effectiveness of the proposed method and compare it to other segmentation algorithms. The efficiency can easily be improved by exploiting the intrinsic parallelism of the proposed method.
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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2002.806256