Shape and Texture Based Classification of Fish Species

  • Rasmus Larsen
  • Hildur Olafsdottir
  • Bjarne Kjær Ersbøll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

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 %.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rasmus Larsen
    • 1
  • Hildur Olafsdottir
    • 1
  • Bjarne Kjær Ersbøll
    • 1
  1. 1.DTU InformaticsTechnical University of DenmarkKgs. LyngbyDenmark

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