Authors:
Akar Taher
;
Kacem Chehdi
and
Claude Cariou
Affiliation:
University of Rennes 1, France
Keyword(s):
Classification, Cooperative, Unsupervised, Nonparametric, Genetic Algorithm, Multicomponent, Image.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Multiclassifier Fusion
;
Pattern Recognition
;
Theory and Methods
Abstract:
In this paper an unsupervised nonparametric cooperative and adaptive approach for multicomponent image partitioning is presented. In this approach the images are partitioned component by component and intermediate classification results are evaluated and fused, to get the final partitioning result. Two unsupervised classification methods are used in parallel cooperation to partition each component of the image. The originality of the approach relies i) on its local adaptation to the type of regions in an image (textured, non-textured), ii) on the automatic estimation of the number of classes and iii) on the introduction of several levels of evaluation and validation of intermediate partitioning results before obtaining the final classification result. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each component and its adjacent components, and finally th
e information of all the components. In our approach, the detected region types are treated separately from the feature extraction step, to the final classification results. The efficiency of our approach is shown on two real applications using a hyperspectral image for the identification of invasive and non invasive vegetation and a multispectral image for pine trees detection.
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