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2009 | OriginalPaper | Chapter

Shape and Texture Based Classification of Fish Species

Authors : Rasmus Larsen, Hildur Olafsdottir, Bjarne Kjær Ersbøll

Published in: Image Analysis

Publisher: Springer Berlin Heidelberg

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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 %.

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Metadata
Title
Shape and Texture Based Classification of Fish Species
Authors
Rasmus Larsen
Hildur Olafsdottir
Bjarne Kjær Ersbøll
Copyright Year
2009
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-02230-2_76

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