Shape and Texture Based Classification of Fish Species

Rasmus Larsen, Hildur Ólafsdóttir, Bjarne Kjær Ersbøll

    Research output: Contribution to journalConference articleResearchpeer-review


    In this paper we conduct a case study of ¯sh 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 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 classfication. In a linear discrimant analysis based on the two best combined modes of variation we obtain a resubstitution rate of 76 %
    Original languageEnglish
    Book seriesLecture Notes in Computer Science
    Pages (from-to)745-749
    Publication statusPublished - 2009
    Event16th Scandinavian Conference on Image Analysis (SCIA) - Oslo, Norway
    Duration: 15 Jun 200918 Jun 2009


    Conference16th Scandinavian Conference on Image Analysis (SCIA)
    Internet address

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