Annotation-free learning of plankton for classification and anomaly detection

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...

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Autores principales:	Vito P. Pastore, Thomas G. Zimmerman, Sujoy K. Biswas, Simone Bianco
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/165c621872b74325a17ffed8475d41a3
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spelling	oai:doaj.org-article:165c621872b74325a17ffed8475d41a32021-12-02T16:26:21ZAnnotation-free learning of plankton for classification and anomaly detection10.1038/s41598-020-68662-32045-2322https://doaj.org/article/165c621872b74325a17ffed8475d41a32020-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-68662-3https://doaj.org/toc/2045-2322Abstract 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.Vito P. PastoreThomas G. ZimmermanSujoy K. BiswasSimone BiancoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vito P. Pastore Thomas G. Zimmerman Sujoy K. Biswas Simone Bianco Annotation-free learning of plankton for classification and anomaly detection
description	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.
format	article
author	Vito P. Pastore Thomas G. Zimmerman Sujoy K. Biswas Simone Bianco
author_facet	Vito P. Pastore Thomas G. Zimmerman Sujoy K. Biswas Simone Bianco
author_sort	Vito P. Pastore
title	Annotation-free learning of plankton for classification and anomaly detection
title_short	Annotation-free learning of plankton for classification and anomaly detection
title_full	Annotation-free learning of plankton for classification and anomaly detection
title_fullStr	Annotation-free learning of plankton for classification and anomaly detection
title_full_unstemmed	Annotation-free learning of plankton for classification and anomaly detection
title_sort	annotation-free learning of plankton for classification and anomaly detection
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/165c621872b74325a17ffed8475d41a3
work_keys_str_mv	AT vitoppastore annotationfreelearningofplanktonforclassificationandanomalydetection AT thomasgzimmerman annotationfreelearningofplanktonforclassificationandanomalydetection AT sujoykbiswas annotationfreelearningofplanktonforclassificationandanomalydetection AT simonebianco annotationfreelearningofplanktonforclassificationandanomalydetection
_version_	1718384036883202048

Annotation-free learning of plankton for classification and anomaly detection

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