earticle

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.