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Automatic benthic imagery recognition using a hierarchical two-stage approach
Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.ORCID-id: 0000-0003-2185-8973
Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
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2018 (engelsk)Inngår i: Signal, Image and Video Processing, ISSN 1863-1703, E-ISSN 1863-1711, Vol. 12, nr 6, s. 1107-1114Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The main objective of this work is to establish an automated classification system of seabed images. A novel two-stage approach to solving the image region classification task is presented. The first stage is based on information characterizing geometry, colour and texture of the region being analysed. Random forests and support vector machines are considered as classifiers in this work. In the second stage, additional information characterizing image regions surrounding the region being analysed is used. The reliability of decisions made in the first stage regarding the surrounding regions is taken into account when constructing a feature vector for the second stage. The proposed technique was tested in an image region recognition task including five benthic classes: red algae, sponge, sand, lithothamnium and kelp. The task was solved with the average accuracy of 90.11% using a data set consisting of 4589 image regions and the tenfold cross-validation to assess the performance. The two-stage approach allowed increasing the classification accuracy for all the five classes, more than 27% for the “difficult” to recognize “kelp” class. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

sted, utgiver, år, opplag, sider
London: Springer, 2018. Vol. 12, nr 6, s. 1107-1114
Emneord [en]
Seabed image segmentation, Machine learning, Supervised classification, Feature extraction, Two-stage classifier
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Identifikatorer
URN: urn:nbn:se:hh:diva-38428DOI: 10.1007/s11760-018-1262-4ISI: 000441392700011Scopus ID: 2-s2.0-85051420102OAI: oai:DiVA.org:hh-38428DiVA, id: diva2:1266087
Tilgjengelig fra: 2018-11-27 Laget: 2018-11-27 Sist oppdatert: 2018-11-27bibliografisk kontrollert

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