Metaheuristics Methods (GA and ACO) for Minimizing the Length of Freeman Chain Code from Handwritten Isolated Characters
This paper presents a comparison of metaheuristic
algorithms, Genetic Algorithm (GA) and Ant Colony Optimization
(ACO), in producing freeman chain code (FCC). The main problem
in representing characters using FCC is the length of the FCC
depends on the starting points. Isolated characters, especially the
upper-case characters, usually have branches that make the traversing
process difficult. The study in FCC construction using one
continuous route has not been widely explored. This is our
motivation to use the population-based metaheuristics. The
experimental result shows that the route length using GA is better
than ACO, however, ACO is better in computation time than GA.
[1] Shi-Fei Ding; Wei-KuanJia; Chun-Yang Su; Zhong-Zhi Shi; Research of
pattern feature extraction and selection. Machine Learning and
Cybernetics, 2008 International Conference on Volume 1, 12-15 July
2008 Page(s):466 - 471.
[2] ZhaoqiBian, Xuegong Zhang. Pattern Recognition. 2nd Edition,
Tsinghua University Press, Beijing, 2000.
[3] Jixiang Sun, Modern pattern recognition, Defense University of Science
and Technology Publishing House, Changsha, 2002.
[4] Kunaver, M.; Tasic, J.F.; Image feature extraction - an overview.
Computer as a Tool, 2005. EUROCON 2005.The International
Conference on Volume 1, 21-24 Nov. 2005 Page(s):183 - 186.
[5] Jähne, Bernd; Digital Image Processing: Concepts, Algorithms, and
Scientific Applications. Edition: 6, Published by Springer, 2005.
[6] Liu, Y.K., Zalik, B.: An efficient chain code with Huffman coding,
Pattern Recognition, 38(4), 2005, 553-557.
[7] Sánchez-Cruz, Hermilo., Bribiesca, Ernesto., Rodríguez-Dagnino, R.M.
Efficiency of Chain Codes to Represent Binary Objects. Volume 40,
Issue 6, June 2007, Pages 1660-1674.
[8] Wulandhari. L.A., HaronHabibolah. The Evolution and Trend of Chain
Code Scheme. ICGST-GVIP, ISSN 1687-398X, Volume (8), Issue (III),
October 2008.
[9] Freeman. H, Techniques for the Digital Computer Analysis of Chain-
Encoded Arbitrary Plane Curves, Proc. Natn. Electron. Conf. 18 (1961)
312-324.
[10] Freeman H, Computer Processing of Line-Drawing Images, ACM
Computing Surveys 6, 1974, 57-97.
[11] Holland, J.H. (1975). Adaptation in Natural and Artificial Systems.
University of Michigan Press, New York.
[12] Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In
Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization.
McGraw-Hill, London (1999) 11-32.
[13] M. Dorigo and G. Di Caro, "The Ant Colony Optimization metaheuristic,"
in New Ideas in Optimization, D. Corne et al., Eds., McGraw
Hill, London, UK, pp. 11-32, 1999.
[14] M. Dorigo, G. Di Caro, and L.M. Gambardella, "Ant algorithms for
discrete optimization," Artificial Life, vol. 5, no. 2, pp. 137-172, 1999.
[15] Dorigo, M., Birattari, M., Stutzle, T., Ant Colony Optimization. IEEE
Computational Intelligence Magazine. November 2006.
[16] Dorigo M (1996) The ant system: optimization by a colony of
cooperating agents. IEEE Trans Syst Man Cybern Part B 26:1-13.
[17] J.-L. Deneubourg, S. Aron, S. Goss, J.-M. Pasteels, The self-organizing
exploratory pattern of the argentine ant, J. Insect Behav. 3 (1990) 159-
168.
[18] Socha K, Dorigo M (2008) Ant colony optimization for continuous
domain. Eur J Oper Res 185:1155-1173.
[19] Engkamat, A.A. Enhancement of Parallel Thinning Algorithm for
Handwritten Characters Using Neural Network. MSc Thesis. Universiti
Teknologi Malaysia, 2005.
[1] Shi-Fei Ding; Wei-KuanJia; Chun-Yang Su; Zhong-Zhi Shi; Research of
pattern feature extraction and selection. Machine Learning and
Cybernetics, 2008 International Conference on Volume 1, 12-15 July
2008 Page(s):466 - 471.
[2] ZhaoqiBian, Xuegong Zhang. Pattern Recognition. 2nd Edition,
Tsinghua University Press, Beijing, 2000.
[3] Jixiang Sun, Modern pattern recognition, Defense University of Science
and Technology Publishing House, Changsha, 2002.
[4] Kunaver, M.; Tasic, J.F.; Image feature extraction - an overview.
Computer as a Tool, 2005. EUROCON 2005.The International
Conference on Volume 1, 21-24 Nov. 2005 Page(s):183 - 186.
[5] Jähne, Bernd; Digital Image Processing: Concepts, Algorithms, and
Scientific Applications. Edition: 6, Published by Springer, 2005.
[6] Liu, Y.K., Zalik, B.: An efficient chain code with Huffman coding,
Pattern Recognition, 38(4), 2005, 553-557.
[7] Sánchez-Cruz, Hermilo., Bribiesca, Ernesto., Rodríguez-Dagnino, R.M.
Efficiency of Chain Codes to Represent Binary Objects. Volume 40,
Issue 6, June 2007, Pages 1660-1674.
[8] Wulandhari. L.A., HaronHabibolah. The Evolution and Trend of Chain
Code Scheme. ICGST-GVIP, ISSN 1687-398X, Volume (8), Issue (III),
October 2008.
[9] Freeman. H, Techniques for the Digital Computer Analysis of Chain-
Encoded Arbitrary Plane Curves, Proc. Natn. Electron. Conf. 18 (1961)
312-324.
[10] Freeman H, Computer Processing of Line-Drawing Images, ACM
Computing Surveys 6, 1974, 57-97.
[11] Holland, J.H. (1975). Adaptation in Natural and Artificial Systems.
University of Michigan Press, New York.
[12] Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In
Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization.
McGraw-Hill, London (1999) 11-32.
[13] M. Dorigo and G. Di Caro, "The Ant Colony Optimization metaheuristic,"
in New Ideas in Optimization, D. Corne et al., Eds., McGraw
Hill, London, UK, pp. 11-32, 1999.
[14] M. Dorigo, G. Di Caro, and L.M. Gambardella, "Ant algorithms for
discrete optimization," Artificial Life, vol. 5, no. 2, pp. 137-172, 1999.
[15] Dorigo, M., Birattari, M., Stutzle, T., Ant Colony Optimization. IEEE
Computational Intelligence Magazine. November 2006.
[16] Dorigo M (1996) The ant system: optimization by a colony of
cooperating agents. IEEE Trans Syst Man Cybern Part B 26:1-13.
[17] J.-L. Deneubourg, S. Aron, S. Goss, J.-M. Pasteels, The self-organizing
exploratory pattern of the argentine ant, J. Insect Behav. 3 (1990) 159-
168.
[18] Socha K, Dorigo M (2008) Ant colony optimization for continuous
domain. Eur J Oper Res 185:1155-1173.
[19] Engkamat, A.A. Enhancement of Parallel Thinning Algorithm for
Handwritten Characters Using Neural Network. MSc Thesis. Universiti
Teknologi Malaysia, 2005.
@article{"International Journal of Information, Control and Computer Sciences:62622", author = "Dewi Nasien and Habibollah Haron and Siti SophiayatiYuhaniz", title = "Metaheuristics Methods (GA and ACO) for Minimizing the Length of Freeman Chain Code from Handwritten Isolated Characters", abstract = "This paper presents a comparison of metaheuristic
algorithms, Genetic Algorithm (GA) and Ant Colony Optimization
(ACO), in producing freeman chain code (FCC). The main problem
in representing characters using FCC is the length of the FCC
depends on the starting points. Isolated characters, especially the
upper-case characters, usually have branches that make the traversing
process difficult. The study in FCC construction using one
continuous route has not been widely explored. This is our
motivation to use the population-based metaheuristics. The
experimental result shows that the route length using GA is better
than ACO, however, ACO is better in computation time than GA.", keywords = "Handwriting Recognition, Feature Extraction,Freeman Chain Code, Genetic Algorithm and Ant ColonyOptimization.", volume = "4", number = "2", pages = "332-6", }
