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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Larsen, R., Olafsdottir, H., Ersbøll, B.K. (2009). Shape and Texture Based Classification of Fish Species. In: Salberg, AB., Hardeberg, J.Y., Jenssen, R. (eds) Image Analysis. SCIA 2009. Lecture Notes in Computer Science, vol 5575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02230-2_76
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DOI: https://doi.org/10.1007/978-3-642-02230-2_76
Publisher Name: Springer, Berlin, Heidelberg
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