A divisive hierarchical k-means based algorithm for image segmentation
| dc.book.title | Intelligent Systems and Knowledge Engineering (ISKE), 2010 International Conference on | |
| dc.contributor.author | Martín H., José Antonio | |
| dc.contributor.author | Montero De Juan, Francisco Javier | |
| dc.contributor.author | Gómez González, Daniel | |
| dc.date.accessioned | 2023-06-20T05:46:37Z | |
| dc.date.available | 2023-06-20T05:46:37Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | In this paper we present a divisive hierarchical method for the analysis and segmentation of visual images. The proposed method is based on the use of the k-means method embedded in a recursive algorithm to obtain a clustering at each node of the hierarchy. The recursive algorithm determines automatically at each node a good estimate of the parameter k (the number of clusters in the k-means algorithm) based on relevant statistics. We have made several experiments with different kinds of images obtaining encouraging results showing that the method can be used effectively not only for automatic image segmentation but also for image analysis and, even more, data mining. | en |
| dc.description.department | Depto. de Estadística e Investigación Operativa | |
| dc.description.faculty | Fac. de Ciencias Matemáticas | |
| dc.description.refereed | TRUE | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/28874 | |
| dc.identifier.citation | H., M.J.A., Montero, J., Yanez, J., Gomez, D.: A divisive hierarchical k-means based algorithm for image segmentation. En: 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering. pp. 300-304. IEEE, Hangzhou (2010) | |
| dc.identifier.doi | 10.1109/ISKE.2010.5680865 | |
| dc.identifier.isbn | 978-1-4244-6791-4 | |
| dc.identifier.officialurl | https//doi.org/10.1109/ISKE.2010.5680865 | |
| dc.identifier.relatedurl | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5680865&abstractAccess=no&userType=inst | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/45547 | |
| dc.language.iso | eng | |
| dc.page.final | 304 | |
| dc.page.initial | 300 | |
| dc.publication.place | Hangzhou | |
| dc.publisher | IEEE | |
| dc.rights.accessRights | restricted access | |
| dc.subject.cdu | 519.8 | |
| dc.subject.keyword | Adaptive k-means | |
| dc.subject.keyword | Computer vision | |
| dc.subject.keyword | Hierarchical clustering | |
| dc.subject.keyword | Image segmentation | |
| dc.subject.ucm | Investigación operativa (Matemáticas) | |
| dc.subject.unesco | 1207 Investigación Operativa | |
| dc.title | A divisive hierarchical k-means based algorithm for image segmentation | en |
| dc.type | book part | |
| dcterms.references | K. S. Fu and J. K. Mui, "A survey on image segmentation," Pattern Recognition, vol. 13, pp. 3-16, 1981, earliest survey of segmentation, mainly thresholding. R. M. Haralick and L. G. Shapiro, "Image segmentation techniques," Computer Vision, Graphics, and Image Processing, vol. 29, pp. 100-132, 1985. N. R. Pal and S. K. Pal, "A review on image segmentation techniques," Pattern Recognition, vol. 26, no. 9, pp. 1277-1294, 1993. D. Gómez, J. Montero, J. Yáñez, and C. Poidomanl, "A graph coloring approach for image segmentation," Omega, vol. 35, no. 2, pp. 173 -183, 2007. H. Zhang, J. E. Fritts, and S. A. Goldman, "Image segmentation evaluation: A survey of unsupervised methods," Computer Vision and Image Understanding, vol. 110, no. 2, pp. 260-280, May 2008. J. A. Hartigan, Clustering Algorithms. New York, NY, USA: John Wiley & Sons, Inc., 1975. J. Scoltock, "A survey of the literature of cluster analysis," The Computer Journal, vol. 25, no. 1, pp. 130-134, Feb. 1982. A. K. Jain, and R. C. Dubes, Algorithms for Clustering Data. Englewood Cliffs: Prentice Hall, 1988. L. Kaufman, Finding groups in data: an introduction to cluster analysis. New York: Wiley, 1990. F. Murtagh, "A survey of recent advances in hierarchical clustering algorithms," Comput. J, vol. 26, no. 4, pp. 354-359, 1983. P. Willet, "Recent trends in hierarchical document clustering: a criticial review," Information Processing and Management, vol. 24, pp. 577-597, 1988. J. S. Nevid, Psychology: Concepts and Applications, 2nd ed. Wadsworth Publishing, 2009. A. M. Triesman and gany Gelade, "A feature-integration theory of attention," ACM Computing Surveys Cognitive Psychology, vol. 12, pp. 97-136, 1980. W. Kohler, Gestalt psychology. New York: Liveright, 1929. J. B. McQueen, "Some methods of classification and analysis of multivariate observations," in Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, Eds., 1967, pp. 281-297. J. A. Hartigan and M. A. Wong, "Algorithm. AS136. A K"-means clustering algorithm," Applied Statistics, vol. 28, pp. 100-108, 1979. J. A. Martin. H., M. Santos, and J. de Lope, "Orthogonal variant moments features in image analysis," Information Sciences, vol. 180, no. 6, pp. 846-860, March 2010. D. Gómez, J. Montero, and J. Yáñez, "A coloring fuzzy graph approach for image classification," Information Sciences, vol. 176, no. 24, pp. 3645-3657, 2006. | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 9e4cf7df-686c-452d-a98e-7b2602e9e0ea | |
| relation.isAuthorOfPublication | 4dcf8c54-8545-4232-8acf-c163330fd0fe | |
| relation.isAuthorOfPublication.latestForDiscovery | 9e4cf7df-686c-452d-a98e-7b2602e9e0ea |
Download
Original bundle
1 - 1 of 1