hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. 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.
Show others and affiliations
2018 (English)In: Signal, Image and Video Processing, ISSN 1863-1703, E-ISSN 1863-1711, Vol. 12, no 6, p. 1107-1114Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
London: Springer, 2018. Vol. 12, no 6, p. 1107-1114
Keywords [en]
Seabed image segmentation, Machine learning, Supervised classification, Feature extraction, Two-stage classifier
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
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
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2018-11-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Verikas, Antanas

Search in DiVA

By author/editor
Verikas, Antanas
By organisation
CAISR - Center for Applied Intelligent Systems Research
In the same journal
Signal, Image and Video Processing
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 35 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf