A hierarchical image segmentation algorithm based on an observation scale

Abstract : Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. In addition, for image segmentation, the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenszwalb and Huttenlocher. Quantitative and qualitative assessments of the method on Berkeley image database shows efficiency, ease of use and robustness of our method.
Type de document :
Communication dans un congrès
Joint IAPR International Workshop, SSPR&SPR 2012, Nov 2012, Japan. 7626, pp.116-125, 2012, Lecture Notes in Computer Science. <10.1007/978-3-642-34166-3_13>
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-00789387
Contributeur : Jean Cousty <>
Soumis le : lundi 18 février 2013 - 10:51:41
Dernière modification le : lundi 18 février 2013 - 15:30:04
Document(s) archivé(s) le : dimanche 19 mai 2013 - 04:02:41

Fichier

GCKN-SSPR2012.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Citation

Silvio Jamil Ferzoli Guimarães, Jean Cousty, Yukiko Kenmochi, Laurent Najman. A hierarchical image segmentation algorithm based on an observation scale. Joint IAPR International Workshop, SSPR&SPR 2012, Nov 2012, Japan. 7626, pp.116-125, 2012, Lecture Notes in Computer Science. <10.1007/978-3-642-34166-3_13>. <hal-00789387>

Partager

Métriques

Consultations de
la notice

244

Téléchargements du document

984