Red Tide Image Recognition using Roundness and Entropy based on Semantic Feature

Red tide is a natural phenomenon that changes sea color by harmful algal bloom. There has been a lot of study on red tide due to increasing of red tide damage. However, to automatically recognize the red tide algae images present in previous studies are not enough. It is difficult to recognize red tide algae images because they do not have matching features for recognizing various species of algae. Besides, the accuracy of algae image recognition is low since previous red tide recognition method mostly uses a few species of red tide harmful algae images for training. In order to resolve the above limitation, this paper proposes the red tide algae recognition method using roundness and entropy based on semantic features of LSA (latent semantic analysis). The experimental results demonstrate that the proposed method achieves better performance than other red tide recognition methods.