Earticle

초록

영어

Red tide is a temporary natural phenomenon involving harmful algal blooms (HABs) in company with a changing sea color from normal to red or reddish brown, and which has a bad influence on coast environments and sea ecosystems. The HABs have inflicted massive mortality on fin fish and shellfish, damaging the economies of fisheries for almost every year from 1990 in South Korea. There has been a lot of study on red tide due to increasing of red tide damage. However, internal study of automatic red tide image classification is not enough. Especially, extraction of matching center of image features for recognizing algae image object is difficult because over 200 species of algae in the world have a different size and features. Besides, the accuracy of algae image recognition of various species is low since previous red tide recognition methods mostly use a few species of red tide harmful algae images for training of classification. In order to resolve the above limitation, this paper proposes the red tide algae image recognition method using rotation of image angle and semantic feature based on NMF (nonnegative matrix factorization). The experimental results demonstrate that the proposed method achieves better performance than other red tide recognition methods.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Non-negative Matrix Factorization
 4. Proposed Method
  4.1. Preprocessing
  4.2. Candidate Image Extraction
  4.3. Recognizing Red Tide Image
 5. Experiment
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Sun Park [ School of Information Communication Engineering, GIST, South Korea ]
• Myeong Soo Choi [ Institute Research of Information Science and Engineering, Mokpo National University, South Korea ]
• Yeonwoo Lee [ Department of Information Communication Engeering, Mokpo National University, South Korea ]
• Seong Ro Lee [ Department of Information Electronics Engineering, Mokpo National University, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605