Automatically finding clusters in normalized cuts

DOI10.1016/j.patcog.2011.01.003zbMath1210.68095OpenAlexW1999201567MaRDI QIDQ716368

Pablo Musé, Mariano Tepper, Andrés Almansa, Marta E. Mejail

Publication date: 28 April 2011

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://hal-imt.archives-ouvertes.fr/hal-00631620/file/clustering-PR2010.pdf

zbMATH Keywords

clusteringa contrario detectionnormalized cuts

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (1)

A review on spectral clustering and stochastic block models

Uses Software

• MegaWave
• BSDS
• Graclus

Cites Work

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• Image segmentation by a contrario simulation
• A unified framework for detecting groups and application to shape recognition
• Consistency of spectral clustering
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• Mean shift spectral clustering
• Efficient graph-based image segmentation
• On clusterings
• Worst-case Analysis of Set Union Algorithms
• The estimation of the gradient of a density function, with applications in pattern recognition
• Model Selection and Multimodel Inference
• Graph Clustering and Minimum Cut Trees
• The minimum description length principle in coding and modeling
• Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
• Edge detection by Helmholtz principle

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