Citation
Ishak, Asnor Juraiza and Che Soh, Azura and Marhaban, Mohammad Hamiruce and Khamis, Shamsul and Ghasab, Mohammad Ali Jan
(2013)
Automated recognition of Ficus deltoidea using ant colony optimization technique.
In: 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), 19-21 June 2013, Melbourne, Australia. (pp. 296-300).
Abstract
Improving in the fields of soft computing and artificial
intelligence, the branch study of automated herb recognition
among plenty of weeds has become challenging issue due to their applications in medicine, food and industry. This paper presents innovative method to improve the accuracy of classification as well the efficiency, such that irrelevant features that make computational complexity are ignored by feature subset selection that is proposed by means of ant colony optimization algorithm (ACO). At first, through image processing specified features are extracted from the Ficus deltoidea leaves such as vein, morphology and texture features and they construct a search space to be chosen for the optimal subset features that is selected by ACO algorithm as support vector machine (SVM) classify them. The experimental results have shown that the proposed method not only optimize feature subset, but also has a remarkable positive impact on accuracy.
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