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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zion, B., Alchanatis, V., Ostrovsky, V., Barki, A., Karplus, I.: Real-time underwater sorting of edible fish species. Computers and Electronics in Agriculture 56, 34–45 (2007)
Rova, A., Mori, G., Dill, L.: One fish, two fish, butterfish, trumpeter: Recognizing fish in underwater video. In: IAPR Conference on Machine Vision Applications (2007)
Lee, D., Archibald, J., Schoenberger, R., Dennis, A., Shiozawa, D.: Contour Matching for Fish Species Recognition and Migration Monitoring. Applications of Computational Intelligence in Biology, 183–207 (2008)
Chambah, M., Semani, D., Renouf, A., Courtellemont, P., Rizzi, A.: Underwater color constancy: enhancement of automatic live fish recognition. In: Color Imaging IX: Processing, Hardcopy, and Applications, vol. 5293, pp. 157–168 (2004)
Cadieux, S., Michaud, F., Lalonde, F.: Intelligent system for automated fish sorting and counting. In: International Conference on Intelligent Robots and Systems, vol. 2, pp. 1279–1284 (2000)
Lillywhite, K., Tippetts, B., Lee, D.J.: Self-tuned evolution-constructed features for general object recognition. Pattern Recognition (in press, corrected proof, 2011)
Roth, P., Winter, M.: Survey of appearance-based methods for object recognition. Technical report, Institute for Computer Graphics and Vision, Graz University of Technology (2008)
Mitchell, M.: An introduction to genetic algorithms. The MIT press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)