Multiple-Way Network Partitioning
IEEE Transactions on Computers
Modeling hypergraphs by graphs with the same mincut properties
Information Processing Letters
Combinatorics and image processing
Graphical Models and Image Processing
Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Application of Adaptive Hypergraph Model to Impulsive Noise Detection
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
On combining graph-partitioning with non-parametric clustering for image segmentation
Computer Vision and Image Understanding
A Parallel Algorithm for Multilevel k-Way Hypergraph Partitioning
ISPDC '04 Proceedings of the Third International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
An in-depth study of graph partitioning measures for perceptual organization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing
An Approximate Distribution for the Normalized Cut
Journal of Mathematical Imaging and Vision
New algorithm for segmentation of images represented as hypergraph hexagonal-grid
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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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.