Benchmark of plankton images classification: emphasizing features extraction over classifier complexity

Abstract. Plankton imaging devices produce vast datasets, the processing of which can be largely accelerated through machine learning. This is a challenging task due to the diversity of plankton, the prevalence of non-biological classes, and the rarity of many classes. Most existing studies rely on small, unpublished datasets that often lack realism in size, class diversity and proportions. We therefore also lack a systematic, realistic benchmark of plankton image classification approaches. To address this gap, we leverage both existing and newly published, large, and realistic plankton imaging datasets from widely used instruments (see Data Availability section for the complete list of dataset DOIs). We evaluate different classification approaches: a classical Random Forest classifier applied to handcrafted features, various Convolutional Neural Networks (CNN), and a combination of both. This work aims to provide reference datasets, baseline results, and insights to guide future endeavors in plankton image classification. Overall, CNN outperformed the classical approach but only significantly for uncommon classes. Larger CNN, which should provide richer features, did not perform better than small ones; and features of small ones could even be further compressed without affecting classification performance. Finally, we highlight that the nature of the classifier is of little importance compared to the content of the features. Our findings suggest that compact CNN (i.e. modest number of convolutional layers and consequently relatively few total parameters) are sufficient to extract relevant information to classify small grayscale plankton images. This has consequences for operational classification models, which can afford to be small and quick. On the other hand, this opens the possibility for further development of the imaging systems to provide larger and richer images.