@CONFERENCE\{IMM2009-05746,
    author       = "R. Larsen and H. Olafsdottir and B. K. Ersb{\o}ll",
    title        = "Shape and texture based classification of fish Species",
    year         = "2009",
    month        = "jun",
    booktitle    = "Proceedings of the Scandinavian Conference on Image Analysis",
    volume       = "",
    series       = "Lecture Notes in Computer Science",
    editor       = "Arnt-B{\o}rre Salberg",
    publisher    = "Springer",
    organization = "",
    address      = "Heidelberg",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5746-full.html",
    abstract     = "In this paper we conduct a case study of fish species classi-
fication based on shape and texture. We consider three fish species: cod,
haddock, and whiting. We derive shape and texture features from an ap-
pearance 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 \%."
}