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Object Recognition Using Artificial Fish Swarm Algorithm on Fourier Descriptors
Current Issue
Volume 2, 2015
Issue 5 (September)
Pages: 105-110   |   Vol. 2, No. 5, September 2015   |   Follow on         
Paper in PDF Downloads: 90   Since Sep. 19, 2015 Views: 1642   Since Sep. 19, 2015
Hamid Ali Abed AL-Asadi, Computers Sciences Department, Education for Pure Science College, University of Basra, Basra, Iraq.
Majida Ali Abed, College of Computers Sciences & Mathematics, University of Tikrit, Tikrit, Iraq.
In this paper, we present an Artificial Fish Swarm Algorithm is a class of an evolutionary optimization technique with three types of classifier combinations using different geometrics’ shape for the recognition of the plant leaves. Fish Swarm Algorithm is applied on Fourier descriptors to get optimum weights that maximize the recognition rate. Fourier descriptors are invariant to rotation, translation or scaling. These optimum Fourier descriptors are then used in process of recognition. The obtained results achieve a recognition rate of 98.75% for Log of Euclidean Distance classifier. Results show that our proposed system advances object recognition with highly effective.
Object Recognition, Artificial Fish Swarm Algorithm (AFSA), Fourier Transform and Classifier
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