Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Image Segmentation by Deep Community Detection Approach

Abstract : To address the problem of segmenting an image into homogeneous communities this paper proposes an efficient algorithm to detect deep communities in the image by maximizing at each stage a new centrality measure, called the local Fiedler vector centrality (LFVC). This measure is associated with the sensitivity of algebraic connectivity to node removals. We show that a greedy node removal strategy, based on iterative maximization of LFVC, has bounded performance loss relative to the optimal, but intractable, combinatorial batch removal strategy. A remarkable feature of this method is the ability to segments the image automatically into homogeneous regions by maximizing the LFVC value in the constructed network from the image. The performance of the proposed algorithm is evaluated on Berkeley Segmentation Database and compared with some well-known methods. Experiments show that the greedy LFVC strategy can efficiently extract deep communities from the image and can achieve much better segmentation results compared to the other known algorithms in terms of qualitative and quantitative accuracy.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01859720
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : mercredi 22 août 2018 - 15:01:18
Dernière modification le : vendredi 17 juillet 2020 - 14:59:10

Identifiants

Citation

Youssef Mourchid, Mohammed El Hassouni, Hocine Cherifi. Image Segmentation by Deep Community Detection Approach. Unet 2017 : International Symposium on Ubiquitous Networking, May 2017, Casablanca, Morocco. pp. 607-618, ⟨10.1007/978-3-319-68179-5_53⟩. ⟨hal-01859720⟩

Partager

Métriques

Consultations de la notice

323