Shape and Texture Based Classification of Fish Species

Authors:
Rasmus Larsen;Hildur Olafsdottir;Bjarne Kjær Ersbøll
Affiliations:
DTU Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark DK-2800;DTU Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark DK-2800;DTU Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark DK-2800
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

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Abstract

In this paper we conduct a case study of fish species classification based on shape and texture. We consider three fish species: cod, haddock, and whiting. We derive shape and texture features from an appearance model of a set of training data. The fish in the training images were manual outlined, and a few features including the eye and backbone contour were also annotated. From these annotations an optimal MDL curve correspondence and a subsequent image registration were derived. We have analyzed a series of shape and texture and combined shape and texture modes of variation for their ability to discriminate between the fish types, as well as conducted a preliminary classification. In a linear discrimant analysis based on the two best combined modes of variation we obtain a resubstitution rate of 76 %.