Recognition and Shape Synthesis of 3-D Objects Based on Attributed Hypergraphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
Spectra of regular graphs and hypergraphs and orthogonal polynomials
European Journal of Combinatorics
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
Algorithms for graph partitioning on the planted partition model
Random Structures & Algorithms
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of Adaptive Hypergraph Model to Impulsive Noise Detection
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
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
Iterative-improvement-based declustering heuristics for multi-disk databases
Information Systems
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Parallel and Distributed Systems
A survey of kernel and spectral methods for clustering
Pattern Recognition
Multi-level direct K-way hypergraph partitioning with multiple constraints and fixed vertices
Journal of Parallel and Distributed Computing
PT-Scotch: A tool for efficient parallel graph ordering
Parallel Computing
How Do Superpixels Affect Image Segmentation?
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Spectral Embedding of Feature Hypergraphs
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Color-texture segmentation using unsupervised graph cuts
Pattern Recognition
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Hypergraph Cuts & Unsupervised Representation for Image Segmentation
Fundamenta Informaticae
Interactive image segmentation using probabilistic hypergraphs
Pattern Recognition
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Hypergraph-Based image representation
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Computer Science Review
Spectral K-way ratio-cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Mathematical morphology on hypergraphs, application to similarity and positive kernel
Computer Vision and Image Understanding
QUAC: Quick unsupervised anisotropic clustering
Pattern Recognition
Random walks in directed hypergraphs and application to semi-supervised image segmentation
Computer Vision and Image Understanding
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In the last few years, hypergraph-based methods have gained considerable attention in the resolution of real-world clustering problems, since such a mode of representation can handle higher-order relationships between elements compared to the standard graph theory. The most popular and promising approach to hypergraph clustering arises from concepts in spectral hypergraph theory [53], and clustering is configured as a hypergraph cut problem where an appropriate objective function has to be optimized. The spectral relaxation of this optimization problem allows to get a clustering that is close to the optimum, but this approach generally suffers from its high computational demands, especially in real-world problems where the size of the data involved in their resolution becomes too large. A natural way to overcome this limitation is to operate a reduction of the hypergraph, where spectral clustering should be applied over a hypergraph of smaller size. In this paper, we introduce two novel hypergraph reduction algorithms that are able to maintain the hypergraph structure as accurate as possible. These algorithms allowed us to design a new approach devoted to hypergraph clustering, based on the multilevel paradigm that operates in three steps: (i) hypergraph reduction; (ii) initial spectral clustering of the reduced hypergraph and (iii) clustering refinement. The accuracy of our hypergraph clustering framework has been demonstrated by extensive experiments with comparison to other hypergraph clustering algorithms, and have been successfully applied to image segmentation, for which an appropriate hypergraph-based model have been designed. The low running times displayed by our algorithm also demonstrates that the latter, unlike the standard spectral clustering approach, can handle datasets of considerable size.