Open-set plankton recognition

Abstract

Research on the plankton community is often dealt with acquiring species-level information of individual micro-organisms. The efficiency is limited when manually analyzing a massive collection of plankton image data generated by modern imaging devices. However, this process can be automated with computer vision and machine learning algorithms to provide results more efficiently. In a practical situation, the model needs to be able to handle unknown class inputs or non-plankton particles which were not present during the training process. This research work focuses on the open-set classification of plankton image data that allows the deep networks to classify or categorize plankton to their respective class, considering the functionality of rejection of unknown class samples. Two open-set methods that are OpenMax and Deep Open Classification (DOC) were implemented with the Convolutional Neural Networks (CNNs) to address this classification task. Based on multiple experiments, the method DOC outperformed the OpenMax for open-set plankton recognition.