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An Improved Iterative Binary Coloring Procedure for Color Image Segmentation

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dc.book.titleFoundations of intelligent systems
dc.contributor.authorMontero De Juan, Francisco Javier
dc.contributor.authorMuñoz López, Susana
dc.contributor.authorGómez González, Daniel
dc.contributor.editorWang, Yinglin
dc.contributor.editorLi, Tianrui
dc.date.accessioned2023-06-20T05:46:28Z
dc.date.available2023-06-20T05:46:28Z
dc.date.issued2012
dc.descriptionProceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011)en
dc.description.abstractn this work we present an improvement on an iterative binary coloring procedure for image segmentation taken from the literature. We introduce some modifications in the way of dealing with the so-called inconsistent pixels, and we show the results obtained by applying both procedures to a satellite image of the province of Seville. The computational experience that we have performed shows that, in general, the modified procedure leads to images of similar or better quality than the ones obtained by the original procedure, as well as to a significant reduction of the number of final regions.en
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/28627
dc.identifier.citationMontero, J., Muñoz, S., Gómez, D.: An Improved Iterative Binary Coloring Procedure for Color Image Segmentation. En: Wang, Y. y Li, T. (eds.) Foundations of Intelligent Systems. pp. 635-640. Springer Berlin Heidelberg, Berlin, Heidelberg (2011)
dc.identifier.doi10.1007/978-3-642-25664-6_74
dc.identifier.officialurlhttps//doi.org/10.1007/978-3-642-25664-6_74
dc.identifier.relatedurlhttp://link.springer.com/chapter/10.1007%2F978-3-642-25664-6_74
dc.identifier.urihttps://hdl.handle.net/20.500.14352/45537
dc.issue.number122
dc.language.isoeng
dc.page.final640
dc.page.initial635
dc.page.total754
dc.publication.placeBerlin
dc.publisherSpringer
dc.relation.ispartofseriesAdvances in Intelligent and Soft Computing
dc.relation.projectIDTIN2009-07901
dc.rights.accessRightsrestricted access
dc.subject.cdu519.22
dc.subject.keywordSegmentation techniques
dc.subject.keywordGraph theory
dc.subject.keywordDecision support systems
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.unesco1209 Estadística
dc.titleAn Improved Iterative Binary Coloring Procedure for Color Image Segmentationen
dc.typebook part
dc.volume.numberII
dcterms.references1. Boskovitz, V., Guterman, H.: An Adaptive Neuro-Fuzzy System for Automatic Image Segmentation and Edge Detection. IEEE Trans. Fuzzy Syst. 10, 247–262 (2002) 2. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image Segmentation Using Expectation-Maximization and its Application to Image Querying. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1026–1038 (2002) 3. Cheng, H.D., Li, J.: Fuzzy Homogeneity and Scale-Space Approach to Color Image Segmentation. Pattern Recognit. 36, 1545–1562 (2003) 4. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. Int. J. Comp. Vis. 59, 167–181 (2004) 5. Gomez, D., Montero, J., Biging, G.: Accuracy Statistics for Judging Soft Classification. Int. J. Remote Sens. 29, 693–709 (2008) 6. Gomez, D., Montero, J., Yañez, J.: A Coloring Fuzzy Graph Approach for Image Segmentation. Inf. Sci. 176, 3645–3657 (2006) 7. Gomez, D., Montero, J., Yañez, J., Poidomani, C.: A Graph Coloring Approach for Image Segmentation. Omega 35, 173–183 (2007) 8. Gomez, D., Yañez, J., Montero, J.: Bi-Criteria Clustering in Networks (submitted,2011) 9. Grady, L.: Random Walks for Image Segmentation. IEEE Trans. Pattern Anal.Mach. Intell. 28, 1768–1783 (2006) 10. Grady, L., Schwartz, E.L.: Isoperimetric Graph Partitioning for Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28, 469–475 (2006) 11. Gross, J., Yellen, J.: Graph Theory and its Applications. CRC Press, Boca Raton (1999) 12. Hung, W.L., Chang, Y.C., Lee, E.S.: Weight Selection in W-K-Means Algorithm with an Application in Color Image Segmentation. Comput. Math. Appl. 62, 668–676 (2011) 13. Lerme, N., L´etocart, L., Malgouyres, F.: Reduced Graphs for Min-Cut/Max-Flow Approaches in Image Segmentation. Electron. Notes Discret. Math. 37, 63–68 (2011) 14. Liu, J., Yang, Y.H.: Multiresolution Color Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 16, 689–700 (1994) 15. Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and Texture Analysis for Image Segmentation. Int. J. Comp. Vis. 43, 7–27 (2001) 16. Martın, J.A., Montero, J., Yañez, J., Gomez, D.: A Divisive Hierarchical k-Means Based Algorithm for Image Segmentation. In: Proc. ISKE 2010, pp. 300–304 (2010) 17. Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000) 18. Tobias, O.J., Seara, R.: Image Segmentation by Histogram Thresholding Using Fuzzy Sets. IEEE Trans. Image Process. 11, 1457–1465 (2002) 19. Wu, Z., Leahy, R.: An Optimal Graph Theoretic Approach to Data Clustering:Theory and its Application to Image Segmentation. IEEE Trans. Pattern Anal.Mach. Intell. 15, 1101–1113 (1993) 20. Yañez, J., Muñoz, S., Montero, J.: Graph Coloring Inconsistencies in Image Segmentation. In: Proc. FLINS 2008, pp. 435–440 (2008)
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