Vitorino Ramos: Genetic Clustering towards Image Segmentation

33. Vitorino Ramos, Genetic Clustering Towards Image Segmentation, Proc. of SIARP´2000 - 5th IberoAmerican Symposium on Pattern Recognition, Fernando Muge, Moisés Piedade & R. Caldas Pinto (Eds.), ISBN 972-97711-1-1, pp. 61-68, Lisbon, Portugal, 11-13 Sep. 2000.

Vitorino Ramos - Genetic Algorithms in Image Segmentation

Vitorino Ramos - Genetic Algorithms in Image Segmentation

Figures - One original image (left - Luanda, Angola map) and two segmentation examples, rivers and roads respectively (Low resolution images).

PDF file: long paper including refs. 34 & 33 (221 Kb)

Abstract: Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or unstationary texture fields. In this work, a method is described for evolving adaptive procedures for these problems. In many real world applications data clustering constitutes a fundamental issue whenever behavioural or feature domains can be mapped into topological domains. We formulate the segmentation problem upon such images as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into Genetic Algorithms, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its intrinsic NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of Genetic Algorithms to automatic and unsupervised texture segmentation.

Keywords: Genetic Algorithms, Colour Image Segmentation, Classification, Clustering, Image Analysis and Processing.

Related Works:

31. Vitorino Ramos, Fernando Muge, Map Segmentation by Colour Cube Genetic K-Mean Clustering, Proc. of ECDL´2000 - 4th European Conference on Research and Advanced Technology for Digital Libraries, J. Borbinha and T. Baker (Eds.), ISBN 3-540-41023-6, Lecture Notes in Computer Science, Vol. 1923, pp. 319-323, Springer-Verlag -Heidelberg, Lisbon, Portugal, 18-20 Sep. 2000.

59. Carlos Fernandes, Vitorino Ramos and Agostinho C. Rosa, Self-Regulated Artificial Ant Colonies on Digital Image Habitats, in Int. Journal of Lateral Computing, IJLC, vol. 2, nº 1, pp. 1-8, ISSN 0973-208X, Dec. 2005.

70. Ramos, V., Fernandes, C., Rosa, A.C., Abraham, A., Computational Chemotaxis in Ants and Bacteria over Dynamic Environments, submitted to CEC´07 - Congress on Evolutionary Computation, IEEE Press, Singapore, 25-28 Sep. 2007.

69. Fernandes, C., Rosa, A.C., Ramos V., Binary Ant Algorithm, to appear in GECCO´07 - Genetic and Evolutionary Computation Conference, ACM Press, London, UK, 7-11 July, 2007.

55. Vitorino Ramos, Pedro Pina, Exploiting and Evolving Rⁿ Mathematical Morphology Feature Spaces, in Ronse Ch., Najman L., Decencière E. (Eds.), Mathematical Morphology: 40 Years On, pp. 465-474, Springer, Dordrecht, The Netherlands, 2005.

51. Vitorino Ramos, Ajith Abraham, Evolving a Stigmergic Self-Organized Data-Mining, in ISDA-04, 4th Int. Conf. on Intelligent Systems, Design and Applications, Budapest, Hungary, ISBN 963-7154-30-2, pp. 725-730, August 26-28, 2004.

53. Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan F. Bendezu and Fionn Murtagh, Swarming around Shellfish Larvae Images, in WCLC-05, 2nd World Congress on Lateral Computing, Bangalore, India, 16-18 Dec., 2005.

29. Vitorino Ramos, Filipe Almeida, Artificial Ant Colonies in Digital Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition, Proceedings of ANTS´2000 - 2nd International Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), Marco Dorigo, Martin Middendorf & Thomas Stüzle (Eds.), pp. 113-116, Brussels, Belgium, 7-9 Sep. 2000.

63. Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes, submitted to A. Porto, A. Pazos, W. Buno (Eds.), Advancing Artificial Intelligence through Biological Process Applications, IDEA Group Inc., 2007.

45. Vitorino Ramos, Ajith Abraham, Swarms on Continuous Data, in CEC´03 - Congress on Evolutionary Computation, IEEE Press, ISBN 078-0378-04-0, pp.1370-1375, Canberra, Australia, 8-12 Dec. 2003.