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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

Author: Oliver i Malagelada, Arnau
Muñoz Pujol, Xavier
Batlle i Grabulosa, Joan
Pacheco Valls, Lluís
Freixenet i Bosch, Jordi
Date: 2006
Abstract: 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
Citation: 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
Document access: http://hdl.handle.net/10256/2425
Language: eng
Publisher: IEEE
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/AQTR.2006.254652
Articles publicats (D-ATC)
Is part of: © IEEE International Conference on Automation, Quality and Testing, Robotics, vol. 2, p. 315-320
Rights: Tots els drets reservats
Subject: Algorismes computacionals
Anàlisi multivariable
Imatges -- Segmentació
Computer algorithms
Imaging segmentation
Multivariate analysis
Title: Improving Clustering Algorithms for Image Segmentation using Contour and Region Information
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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