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Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

© IEEE International Conference on Automation, Quality and Testing, Robotics, vol. 2, p. 315-320

IEEE

Autor: Oliver i Malagelada, Arnau
Muñoz Pujol, Xavier
Batlle i Grabulosa, Joan
Pacheco Valls, Lluís
Freixenet i Bosch, Jordi
Data: 2006
Resum: In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach
Format: application/pdf
Cita: Oliver, A., Munoz, X., Batlle, J., Pacheco, L., i Freixenet, J. (2006). Improving Clustering Algorithms for Image Segmentation using Contour and Region Information. IEEE International Conference on Automation, Quality and Testing, Robotics : 2006, 2, 315-320. Recuperat 26 maig 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4022975
ISSN: 4244-0360-X
Accés al document: http://hdl.handle.net/10256/2425
Llenguatge: eng
Editor: IEEE
Col·lecció: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/AQTR.2006.254652
Articles publicats (D-ATC)
És part de: © IEEE International Conference on Automation, Quality and Testing, Robotics, vol. 2, p. 315-320
Drets: Tots els drets reservats
Matèria: Algorismes computacionals
Anàlisi multivariable
Imatges -- Segmentació
Computer algorithms
Imaging segmentation
Multivariate analysis
Títol: Improving Clustering Algorithms for Image Segmentation using Contour and Region Information
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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