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http://dx.doi.org/10.6109/jkiice.2022.26.7.972

Ensemble Model Based Intelligent Butterfly Image Identification Using Color Intensity Entropy  

Kim, Tae-Hee (Department of Information Security, Dongshin University)
Kang, Seung-Ho (Department of Information Security, Dongshin University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.7, 2022 , pp. 972-980 More about this Journal
Abstract
The butterfly species recognition technology based on machine learning using images has the effect of reducing a lot of time and cost of those involved in the related field to understand the diversity, number, and habitat distribution of butterfly species. In order to improve the accuracy and time efficiency of butterfly species classification, various features used as the inputs of machine learning models have been studied. Among them, branch length similarity(BLS) entropy or color intensity entropy methods using the concept of entropy showed higher accuracy and shorter learning time than other features such as Fourier transform or wavelet. This paper proposes a feature extraction algorithm using RGB color intensity entropy for butterfly color images. In addition, we develop butterfly recognition systems that combines the proposed feature extraction method with representative ensemble models and evaluate their performance.
Keywords
Butterfly Identification; Color Intensity Entropy; Branch Length Similarity Entropy; Ensemble Model;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 IPBES, "The IPBES regional assessment report on biodiversity and ecosystem services for Europe and Central Asia," IPBES, Germany, 2018.
2 S. H. Kang, J. H. Cho, and S. H. Lee, "Identification of butterfly based on their shapes when viewed from different angles using an artificial neural network," Journal of Asia-Pacific Entomology, vol. 17, no. 2, pp.143-149, Jun. 2014.   DOI
3 P. J. D. Weeks and K. J. Gaston, "Image analysis, neural networks, and the taxonomic impediment to biodiversity studies," Biodiversity&Conservation, vol. 6, pp. 263-274, Feb. 1997.   DOI
4 S. H. Kang, S. H. Song, and S. H. Lee, "Identification of butterfly species using a single neural network system," Journal of Asia-Pacific Entomology, vol. 15, no. 3, pp. 431-435, Sep. 2012.   DOI
5 G. W. Hopkins and R. P. Freckleton, "Declines in the numbers of amateur and professional taxonomists: implications for conservation," Animal Conservation, vol. 5, no. 3, pp. 245-249, Aug. 2002.   DOI
6 B. A. Bakri, Z. Ahmad, and S. M. Hatim, "Butterfly Family Detection and Identification Using Convolutional Neural Network for Lepidopterology," International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2S11, pp. 635-640, Sep. 2019.   DOI
7 G. Ke, Q. Meng, T. Finley, T. Wang, W. Ma, Q. Ye, and T. -Y. Liu, "LightGBM: A Highly Efficient Gradient Boosting Decision Tree," in Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach: CA, USA, pp. 3149-3157, 2017.
8 T. Xi, J. Wang, Y. Han, C. Lin, and L. Ji, "Multiple butterfly recognition based on deep residual learning and image analysis," Entomological Research, vol. 52, no. 1, pp. 44-53, Jan. 2021.
9 D. Xin, Y. W. Chen, and J. Li, "Fine-Grained Butterfly Classification in Ecological Images Using Squeeze-And-Excitation and Spatial Attention Modules," Applied Sciences, vol. 10, no. 5, p. 1681, Mar. 2020.   DOI
10 P. J. D. Weeks, M. A. O'Neill, K. J. Gaston, and I. D. Gauld, "Automating insect identification: exploring the limitations of a prototype system," Journal of Applied Entomology, vol. 123, pp. 1-8, Jan. 1999.   DOI
11 S. H. Kang and T. H. Kim, "A Performance Improvement of Automatic Butterfly Identification Method Using Color Intensity Entropy," The Journal of the Korea Contents Association, vol. 17, no. 5, pp. 624-632, May. 2017.   DOI
12 T. K. Ho, "Random Decision Forests," in Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal: QC, Canada, pp. 278-282, 1995.
13 LightGBM [Internet]. Available: https://lightgbm.readthedocs.io/en/latest/index.html.