Abstract
Assessing the population, size, and taxonomy of fish is important in order to manage fish populations, regulate fisheries, and evaluate the impact of man made structures such as dams. Automating this process saves valuable resources of time, money, and manpower. Current methods for automatic fish monitoring rely on a human expert to design features necessary for classifying fish into a taxonomy. This paper describes a method using Evolution-COnstructed (ECO) features to automatically find features that can be used to classify fish into a taxonomy. ECO features provide features automatically and thus can quickly be adapted to new environments and fauna. The effectiveness of ECO features is shown on a dataset of four fish species where using five fold cross validation an average classification rate of 99.4% is achieved.
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© 2011 Springer-Verlag Berlin Heidelberg
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Lillywhite, K., Lee, DJ. (2011). Automated Fish Taxonomy Using Evolution-COnstructed Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_50
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DOI: https://doi.org/10.1007/978-3-642-24028-7_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24027-0
Online ISBN: 978-3-642-24028-7
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