Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Ensemble Model Based Intelligent Butterfly Image Identification Using Color Intensity Entropy

컬러 영상 색채 강도 엔트로피를 이용한 앙상블 모델 기반의 지능형 나비 영상 인식

  • Kim, Tae-Hee (Department of Information Security, Dongshin University) ;
  • Kang, Seung-Ho (Department of Information Security, Dongshin University)
  • Received : 2022.05.24
  • Accepted : 2022.06.18
  • Published : 2022.07.31
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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.

영상을 이용한 기계학습 기반의 나비 종 인식 기술은 나비 종의 다양성 및 개체 수, 종의 서식 분포 등을 파악하는데 관련 분야 종사자의 많은 시간과 비용 감소의 효과를 가져온다. 나비 종 분류의 정확성과 시간 효율을 높이기 위해 기계학습 모델의 입력으로 사용되는 여러 가지 특징들이 연구되었다. 그중 엔트로피 개념을 이용한 가지 길이 유사성 엔트로피나 색채 강도 엔트로피 방법이 푸리에 변환이나 웨이블릿 등 다른 특징들에 비해 높은 정확성과 적은 학습 시간을 보여주었다. 본 논문은 나비의 컬러 영상에 대한 RGB 색채 강도 엔트로피를 이용한 특징 추출 알고리즘을 제안한다. 또한 제안한 특징 추출 방법과 대표적인 앙상블 모델들을 결합한 나비 인식 시스템을 개발하고 성능을 평가한다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea under Grant NRF-2020R1I1A3071599.

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