International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume5,April 2018,

Topic : Comparison of Leaf Recognition using Multi-layer Perceptron and Support Vector Machine

Authors:Juby George || Gladston Raj S

Abstract:Identification of leaves from digital images using various automatic pattern recognition algorithms results in performance degradations. Here, various leaf features are been extracted and exposed to Multi-layer Perceptron and Support Vector Machine. The leaf images are taken from the Columbia Dataset and that are preprocessed to get the region of interest (ROI). The shape, color and vein features are then extracted from the selected ROI. The prominent features are then found out by using Principal Component Analysis (PCA). The reduced feature sets are supplied to these algorithms for identification. Total 150 samples were taken from this dataset spreads over 10 different leaf species. Multilayer Perceptron and Support vector machine are trained with 104 leaf images and are been tested and validated using 23 leaf images each. A comparison is made between the performances of these two learning methods and found that the leaf recognition accuracy of the support vector machine is better than that of Multilayer Perceptron algorithm.

Keywords: Feature extraction, Shape features, Vein features, Wiener filter, Color features, Principal Component Analysis, Multi-layer feed forward network, Support Vector Machine

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DOI: 01.1617/vol5/iss4/pid47065


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Editor-in-Chief

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Dr. Allon Guez
Professor, Drexel University,
USA


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IMPACT FACTOR: 4.890

ISSN(Online):2394-2320

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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ACCEPTANCE RATIO: 28.69%
ARTICLES PUBLISHED:0521
PAPER RECEIVED:01730
Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
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