Novel consensus approaches to the reliable ranking of features for seabed imagery classification

Richard Harrison, Roger Birchall, Dave Mann, Wenjia Wang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Feature saliency estimation and feature selection are important tasks in machine learning applications. Filters, such as distance measures are commonly used as an efficient means of estimating the saliency of individual features. However, feature rankings derived from different distance measures are frequently inconsistent. This can present reliability issues when the rankings are used for feature selection. Two novel consensus approaches to creating a more robust ranking are presented in this paper. Our experimental results show that the consensus approaches can improve reliability over a range of feature parameterizations and various seabed texture classification tasks in sidescan sonar mosaic imagery.
Original languageEnglish
Article number1250026
Number of pages18
JournalInternational Journal of Neural Systems
Issue number6
Publication statusPublished - Dec 2012


  • Feature ranking
  • distance measures
  • consensus methods
  • seabed texture classification

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