Treffer: Unsupervised image segmentation using a hierarchical clustering selection process
Title:
Unsupervised image segmentation using a hierarchical clustering selection process
Source:
Structural, syntactic, and statistical pattern recognition (joint IAPR international workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006)0SSPR 2006. :799-807
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 13 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse statistique, Statistical analysis, Análisis estadístico, Analyse structurale, Structural analysis, Análisis estructural, Analyse syntaxique, Syntactic analysis, Análisis sintáxico, Apprentissage non supervisé, Unsupervised learning, Classification hiérarchique, Hierarchical classification, Clasificación jerarquizada, Classification non supervisée, Unsupervised classification, Clasificación no supervisada, Détection multispectrale, Multispectral detection, Detección multiespectral, Fouille donnée, Data mining, Busca dato, Groupage, Grouping, Agrupamiento, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Segmentation image, Image segmentation, Traitement image, Image processing, Procesamiento imagen
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Dept. Lenguajes y Sistemas Informaticos, Jaume I Univerisity, Spain
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19152037
Database:
PASCAL Archive
Weitere Informationen
In this paper we present an unsupervised algorithm to select the most adequate grouping of regions in an image using a hierarchical clustering scheme. Then, we introduce an optimisation approach for the whole process. The grouping method presented is based on the maximisation of a measure that represents the perceptual decision. The whole strategy takes profit from a hierarchical clustering to find a maximum of the proposed criterion. The algorithm has been used to segment real images as well as multispectral images achieving very accurate results on this task.