Identification of butterfly based on their shapes when viewed from different angles using an artificial neural network
Identification of butterfly species is essential because they are directly associated with crop plants used for human and animal consumption. However, the widely used reliable methods for butterfly identification are not efficient due to complicated butterfly shapes. We previously developed a novel...
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Published in: | Journal of Asia-Pacific entomology Vol. 17; no. 2; pp. 143 - 149 |
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Main Authors: | , , |
Format: | Journal Article |
Language: | English |
Published: |
Elsevier B.V
01-06-2014
한국응용곤충학회 |
Subjects: | |
Online Access: | Get full text |
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Summary: | Identification of butterfly species is essential because they are directly associated with crop plants used for human and animal consumption. However, the widely used reliable methods for butterfly identification are not efficient due to complicated butterfly shapes. We previously developed a novel shape recognition method that uses branch length similarity (BLS) entropy, which is a simple branching network consisting of a single node and branches. The method has been successfully applied to recognize battle tanks and characterize human faces with different emotions. In the present study, we used the BLS entropy profile (an assemble of BLS entropies) as an input feature in a feed-forward back-propagation artificial neural network to identify butterfly species according to their shapes when viewed from different angles (for vertically adjustable angle, θ=±10°, ±20°, …, ±60° and for horizontally adjustable angle, φ=±10°, ±20°, …, ±60°). In the field, butterfly images are generally captured obliquely by camera due to butterfly alignment and viewer positioning, which generates various shapes for a given specimen. To generate different shapes of a butterfly when viewed from different angles, we projected the shapes captured from top-view to a plane rotated through angles θ and φ. Projected shapes with differing θ and φ values were used as training data for the neural network and other shapes were used as test data. Experimental results showed that our method successfully identified various butterfly shapes. In addition, we briefly discuss extension of the method to identify more complicated images of different butterfly species.
•We suggested a novel method that uses BLS entropy profile for butterfly identification.•Our method successfully identified butterfly shapes when viewed from different angles.•We discussed the extension of the method to identify more complicated images of butterfly species. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 G704-000694.2014.17.2.005 |
ISSN: | 1226-8615 1876-7990 |
DOI: | 10.1016/j.aspen.2013.12.004 |