Multiple order gradient feature for macro-invertebrate identification using support vector machines

Authors:
Ville Tirronen;Andrea Caponio;Tomi Haanpää;Kristian Meissner
Affiliations:
Department of Mathematical Information Technology, University of Jyväskylä;Department of Mathematical Information Technology, University of Jyväskylä and Department of Electrotechnics and Electronics, Technical University of Bari;Department of Mathematical Information Technology, University of Jyväskylä;Finnish Environmental Institute, Jyväskylä Unit
Venue:
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Year:
2009

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Abstract

This paper investigates the feasibility of automated benthic macro-invertebrate taxon identification based on support vector machines and a novel gradient based feature. Biomonitoring can efficiently pinpoint subtle environmental changes and is therefore globally widely used in ecological status assessment. However, all biomonitoring is costintensive due to the expert work needed to identify organisms. To relieve this problem an automated image recognition system for benthic macro-invertebrate taxonomical analysis is proposed in this work. Using a novel approach, we present high accuracy classification results, suggesting that automated taxa recognition for benthic macro-invertebrates is viable. Our study indicates that automated image recognition techniques can match human taxonomic identification accuracy and greatly reduce the costs of future taxonomic analysis.