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     Assessing the taxonomy of fish is important in order to manage fish populations, regulate fisheries, and remove the exotic invasive species. 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 work uses a method using Evolution-COnstructed (ECO) features to automatically find features that can be used to classify fish into a taxonomy. Rather than relying on human experts to build features sets to tune their parameters, our method uses simulated evolution to construct series of transforms that convert the input signal of raw pixels of fish images into high quality features. 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. Although we used four fish species to prove feasibility, this method can be easily adapted to new fauna and circumstances.

Fourth place in the Michigan State's Great Lakes Invasive Carp Challenge.    Michigan Department of Natural Resources

Collaborators: Dr. Dong Zhang, Sun Yat-sen University, Guangzhou, China

Graduate Students:

Kirt Lillywhite and Beau Tippetts

  1. D. Zhang, D.J. Lee, M. Zhang, B.J. Tippetts, and K.D. Lilywhite, "Object Recognition Algorithm for the Automatic Identification and Removal of Invasive Fish, “ Biosystems and Engineering, vol. 145, p. 65-75, May 2016.
  2. D. Zhang, K.D. Lillywhite, D.J. Lee, and B.J. Tippetts, “Automated Fish Taxonomy using Evolution-COnstructed Features for invasive species removal,” Pattern Analysis and Applications, vol. 18/2. p. 451-459, May 2015.
  3. K.D. Lillywhite and D.J. Lee, “Automated Fish Taxonomy using Evolution-COnstructed Features,” Lecture Notes in Computer Science (LNCS), Part I, LNCS 6938, p. 541-550, International Symposium on Visual Computing (ISVC), Las Vegas, NV, U.S.A., September 26-28, 2011.
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