The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
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A Performance Improvement of Automatic Butterfly Identification Method Using Color Intensity Entropy

영상의 색체 강도 엔트로피를 이용한 나비 종 자동 인식 향상 방법

  • Received : 2017.04.11
  • Accepted : 2017.05.10
  • Published : 2017.05.28
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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.

영상을 이용한 나비 종 자동 인식 기법은 생물종 다양성 연구 및 종의 진화, 발달 과정의 연구를 위한 기초 작업을 돕는 것으로 연구자들의 관심이 높다. 기계학습 기반의 나비 종 인식 시스템은 사용하는 특징 추출 방법에 성능이 크게 좌우되는 성질을 가지고 있다. 본 논문은 나비 영상이 가진 색채 강도의 분포를 이용하는 색채 강도 (Color Intensity) 엔트로피를 제안하고 기존에 제시된 가지 길이 유사성 (Branch Length Similarity) 엔트로피와 함께 사용할 경우 10% 이상의 인식률 향상을 얻을 수 있음을 보인다. 제안한 방법의 신뢰성 있는 성능 평가를 위해 영상 인식에 자주 사용되는 대표적인 특징 추출 방법인 아이겐 이미지, 2D 푸리에 변환, 2D 웨이블릿 변환 방법들을 비교 대상으로 다양한 기계학습을 이용해 성능을 평가한다.

Keywords

References

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