The week's pick
Random Articles
Reseach Article
AntHeaps: A New Hybrid Image Segmentation Algorithm using Ant Colonies
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 81 - Number 3 |
| Year of Publication: 2013 |
| Authors: Asmaa Reda, Hala Abdel-galil, Atef Z. Ghalwash |
10.5120/13994-2017
|
Asmaa Reda, Hala Abdel-galil, Atef Z. Ghalwash . AntHeaps: A New Hybrid Image Segmentation Algorithm using Ant Colonies. International Journal of Computer Applications. 81, 3 ( November 2013), 36-40. DOI=10.5120/13994-2017
Abstract
During the last few years, the Segmentation problem has been tackled from different disciplines. Many algorithms have been developed to solve this problem. AntClust algorithm is an ant-based algorithm that uses the self-organizing and autonomous brood sorting behavior observed in real ants for unsupervised partitioning. A population of artificial ants provides an image segmentation of the relevant classes without any previous knowledge about the number of classes needed. This paper proposes a hybrid solution based on AntClust algorithm and data mining (e. g. , Kmeans). Experimental results demonstrate that the proposed solution is able to extract the correct number of clusters with better clustering quality and execution time compared to the results obtained from AntClust algorithm.
References
- S. Quadfel and M. Batouche, "An Efficient Ant Algorith for Swarm-Based Image Clustering," Journal of Computer Science, vol. 3, no. 3, pp. 162-167, 2007.
- M. Dorigo, M. Birattari and T. Stützle, "Ant Colony Optimization, Artificial Ants as a Computational Intelligence Technique," IEEE Computational Intelligence Magazine, pp. 28-39, November 2006.
- J. Deneubourg, S. Goss and N. Franks, "The Dynamic of Collective Sorting Robot-like Ants and Ant-like Robots," in the first conference on Simulation of Adaptive Behaior, 1990.
- E. Lumer and B. Faieta, "Diversity and Adaptation in Populations of Clustering Ants," in The 3rd International Conference on the Simulation of Adaptive Behavior, 1994.
- P. Kuntz and D. and Snyers, "Emergent colonization and graph partitioning," in the third Tnternational Conference on simulation of Adabtive Behavior, 1994.
- N. Monmarche, "On Data Clustering with Artificial Ants," Data Mining with Evolutionary Algorithms: Research Directions – Papers from the AAAI Workshop, pp. 23-26, 1999.
- S. Alpert, M. Galun, R. Basri and A. Brandt, "Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007.
- M. Hameurlaine, A. Moussaoui and H. Cherroun, "AntMeans: A New Hybrid Algorithm based on Ant Colonies for Complex Data Mining," International Journal of Computer Applications (0975 – 8887), vol. 60, no. 17, December 2012.
- M. Chong and . M. Munusamy, "A Hybrid Ant-Based Clustering Algorithm," The International Federation for Information Processing, vol. 187, pp. 247-256, 2005.
- O. A. Mohamed and R. Sivakumar, "Ant-based Clustering Algorithms: A Brief survay," International Journal of Computer Theory and Engineering, vol. 2, no. 5, pp. 787-796, October 2010.
- J. Smaldon and A. A. Freitas, "A New Version of the Ant-Miner Algorithm Discovering Unordered Rule Sets," in The 8th annual conference on Genetic and evolutionary computation, New York, 2006.
- W. Omar, A. Badr and A. E. -F. Hegazy, "Hybrid Ant-Based Clustering Algorithm with Cluster Analysis Teshniques," Journal of Computer Science, vol. 9, no. 6, pp. 780-793, september 2013.
- M. Dorigo and T. Stützle, Ant Colony Optimization, Massachusetts Institute of Technology, 2004.
- M. Dorigo and C. Blum, "Ant colony optimization theory: A survey," Theoretical Computer Science, vol. 344, p. 243 – 278, 2004.
- N. Labroche, C. Guinot and . G. Venturini, "Fast Unsupervised Clustering with Artificial Ants," Lecture Notes in Computer Science, vol. 3242, pp. 1143-1152, 2004.
Index Terms
Keywords