Semi-supervised learning for plankton image classification

The production of big data from plankton populations has become feasible with the use of imaging devices. This opens up the possibility of testing key characteristics in planktonic systems. The manual labeling of images is complicated as well as time-consuming. On the other hand, semi-supervised learning makes it possible to automate the process by performing the classification of large unlabeled data with the available labeled data. The classification of plankton images using semi-supervised methods including label propagation, label spreading, and Pseudo labeling utilizing CNN based image features has been implemented in this study. The comparison of the accuracy of the semi-supervised methods and the CNN method using different datasets shows that the semi-supervised learning methods were more accurate than the baseline method where the amount of data available to train the model was sufficient and fully supervised CNN was able to extract the correct features. However, sometimes the semi-supervised learning methods have not been more accurate than CNN, when CNN has struggled to find the right image features.