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bioRxiv · 10.1101/856815

Annotation-free Learning of Plankton for Classification and Anomaly Detection

PASTORE, V. P.·ZIMMERMAN, T.·BISWAS, S.·BIANCO, S.
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Abstract

The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors. Author SummaryPlankton are at the bottom of the aquatic food chain and marine phytoplankton are estimated to be responsible for over 50% of all global primary production [1] and play a fundamental role in climate regulation. Thus, changes in plankton ecology may have a profound impact on global climate, as well as deep social and economic consequences. It seems therefore paramount to collect and analyze real time plankton data to understand the relationship between the health of plankton and the health of the environment they live in. In this paper, we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. The proposed pipeline is designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.

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PASTORE, V. P., ZIMMERMAN, T., BISWAS, S., BIANCO, S.. 2019-11-27. Annotation-free Learning of Plankton for Classification and Anomaly Detection. https://doi.org/10.1101/856815

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