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http://dx.doi.org/10.5392/JKCA.2017.17.05.624

A Performance Improvement of Automatic Butterfly Identification Method Using Color Intensity Entropy  

Kang, Seung-Ho (동신대학교 정보보안학과)
Kim, Tae-Hee (동신대학교 정보보안학과)
Publication Information
The Journal of the Korea Contents Association / v.17, no.5, 2017 , pp. 624-632 More about this Journal
Abstract
Automatic butterfly identification using images is one of the interesting research fields because it helps the related researchers studying species diversity and evolutionary and development process a lot in this field. The performance of the butterfly species identification system is dependent heavily on the quality of selected features. In this paper, we propose color intensity (CI) entropy by using the distribution of color intensities in a butterfly image. We show color intensity entropy can increase the recognition rate by 10% if it is used together with previously suggested branch length similarity entropy. In addition, the performance comparison with other features such as Eigenface, 2D Fourier transform, and 2D wavelet transform is conducted against several well known machine learning methods.
Keywords
Butterfly; dentification; Feature Extraction; Branch Length Similarity Entropy; Color Intensity Entropy; Machine Learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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