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Abstract
The aim of this paper is to present an improvement of a previously published algorithm. The proposed approach is performed in two steps. In the first step, we generate the Weighted Adaptive Neighborhood Hypergraph (WAINH) of the given gray-scale image. In the second step, we partition the WAINH using a multilevel hypergraph partitioning technique. To evaluate the algorithm performances, experiments were carried out on medical and natural images. The results show that the proposed segmentation approach is more accurate than the graph based segmentation algorithm using normalized cut criteria.
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Authors and Affiliations
LIRIS CNRS, Lyon II University, Lyon, France
Soufiane Rital & Serge Miguet
LIRSIA, University of Bourgogne, Dijon, France
Hocine Cherifi
- Soufiane Rital
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- Hocine Cherifi
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- Serge Miguet
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Editors and Affiliations
Research School of Infomatics, Loughborough, UK
Sameer Singh
ATR Lab, Research School of Informatics, University of Loughborough, Loughborough, UK
Maneesha Singh
IBM Corporation, 1133 Wetchester Avenue, White Plains, 10604, New York, United States
Chid Apte
Institute of Computer Vision and applied Computer Sciences, IBaI, Germany
Petra Perner
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Rital, S., Cherifi, H., Miguet, S. (2005). Weighted Adaptive Neighborhood Hypergraph Partitioning for Image Segmentation. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_58
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