Shape and texture based classification of fish Species |
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| 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 %. |
| Type | Conference paper [With referee] |
| Conference | Proceedings of the Scandinavian Conference on Image Analysis |
| Editors | |
| Year | 2009 Month June |
| Publisher | Springer |
| Address | Heidelberg |
| Series | Lecture Notes in Computer Science |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics |