A rotation-invariant feature space according to environmental applications needs in a data mining system using fish otoliths

Abstract

Otoliths are microcalcifications found in the inner ear of fishes and their shape can be analyzed to determine sex, age, populations and species, and thus they can provide necessary and relevant information for ecological and environmental studies. This paper compares two different approaches to deal with an arbitrary rotation of a query image in a data mining system that classifies fish otoliths from their images according to the needs of a real environmental application. The different approaches proposed in this paper will allow the successful use of images that are not normalized or are normalized in a different positioning than the images in the database, something that could be very useful in real field applications. The first approach tested is based on a Rotation-invariant Feature Space derived from the Elliptical Fourier Descriptors (EFD), and the second approach relies on a rotation estimation module; in both cases the data mining system is based in a multiclass classifier implemented in this test case with a simple k-Nearest Neighbours after a complex pre-processing step that provides the Feature extraction.

Keywords

Rotation invariant features feature space multiclass classifiers Elliptic Fourier Descriptor (EFD)pattern matching pre-processing data mining otoliths

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