Annotation-free learning of plankton for classification and anomaly detection.

Scientific Reports
Vito Paolo PastoreSimone Bianco

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. Additionally, we introduce a new algorithm to perform anomaly detection on unclassified samples. Here an anomaly is defined as a significant deviation from the established classification. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.

References

Apr 3, 2001·Science·M J BehrenfeldN H Pollack
Jul 7, 2001·Neural Computation·B SchölkopfR C Williamson
Mar 30, 2013·BMC Bioinformatics·Katja SchulzeMarcus Frohme
Feb 13, 2019·Science Robotics·Trygve O FossumKanna Rajan

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Methods Mentioned

BETA
PCA
features extraction
feature extraction

Software Mentioned

ZooplanktoNet
mathop
KERAS
TensorFlow
DeepSea

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