A Novel Ensemble of Distance Measures for Feature Evaluation: Application to Sonar Imagery

Richard Harrison, Roger Birchall, Dave Mann, Wenjia Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Mapping interesting regions in qualitative sidescan sonar imagery predominantly relies on an expensive human interpretation process. It would therefore be useful to automate components of this task with a feature-based, Machine Learning system. We must first establish a framework for reliably and efficiently evaluating the features. A novel ensemble of probabilistic distance measures is proposed, as an objective function for this purpose. The idea is motivated by the fact that different distance measures yield conflicting feature ranking results. In the ensemble, distances can be combined to produce a consensus rank score. As a test case, we find a sub-optimal parameterisation of a Co-occurrence Matrix, for identifying textures peculiar to the tube-building worm, Sabellaria spinulosa. A strong correlation is found between ensemble scores and classification accuracies. The proposed methodology is applicable to any sonar imagery, classification task or feature groups.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2011
EditorsHujun Yin, Wenjia Wang, Victor Rayward-Smith
Number of pages10
ISBN (Electronic)978-3-642-23878-9
ISBN (Print)978-3-642-23877-2
Publication statusPublished - 2011

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