Automated recognition of Ficus deltoidea using ant colony optimization technique

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.

[1]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[2]  Ahmed Al-Ani,et al.  Feature Subset Selection Using Ant Colony Optimization , 2008 .

[3]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[4]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[5]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[6]  A. Ismail,et al.  Ficus deltoidea: A Potential Alternative Medicine for Diabetes Mellitus , 2012, Evidence-based complementary and alternative medicine : eCAM.

[7]  E. Corner.  The Complex of Ficus deltoidea; A Recent Invasion of the Sunda Shelf , 1969 .

[8]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[9]  S. Moin,et al.  Evaluation of the antinociceptive activity of Ficus deltoidea aqueous extract. , 2008, Fitoterapia.

[10]  M. Abdulla,et al.  Role of Ficus deltoidea extract in the enhancement of wound healing in experimental rats , 2010 .

[11]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[12]  Yixin Chen,et al.  Image Feature Selection Based on Ant Colony Optimization , 2011, Australasian Conference on Artificial Intelligence.

[13]  Ethem Alpaydin,et al.  Support Vector Machines for Multi-class Classification , 1999, IWANN.

[14]  S. N. Geethalakshmi,et al.  An Optimal Feature Subset Selection for Leaf Analysis , 2012 .

[15]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[16]  De-shuang Huang,et al.  Computer-Aided Plant Species Identification (CAPSI) Based on Leaf Shape Matching Technique , 2006 .