Keywords
Otoliths, Feature space, Classifiers, Automatic Taxon Identification (ATI), Elliptic Fourier Descriptors (EFD), Pattern Matching, Pre-processing, Data mining
Location
Session G2: Data Mining for Environmental Sciences
Start Date
17-6-2014 9:00 AM
End Date
17-6-2014 10:20 AM
Abstract
This paper analyzes the characteristics of a rotation-invariant Feature space to be used in a classifier of fish otoliths, it is compared to two other Feature spaces, one with raw data and another with transformed data (using the Elliptic Fourier Descriptors EFD). Otoliths are found in the inner ear of fish. Their shape can be analyzed to determine sex, age, populations and species, and thus they can provide necessary and relevant information for ecological studies. The Automatic Taxon Identifier (ATI) is used to classify fish otoliths directly from a query image and is implemented on-line in a Public Database. This new Features space proposed in this study will allow the use of query images that are not normalized or are normalized in a different pose than the images in the Database. The rotation-invariant Feature space is the pre-processing part of a classifier based in kNearestNeighbours, and it is derived from the Elliptical Fourier Descriptors (EFD). The new Feature space compacts the information, and could be used when a feature reduction strategy is recommended. The authors analyze the classification results in a Test Database applying a controlled rotation to the Query images, and discuss their possible utility of the technique in the future ATI system.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Pre-processing techniques applied to Automatic Taxon Identification on fish otoliths
Session G2: Data Mining for Environmental Sciences
This paper analyzes the characteristics of a rotation-invariant Feature space to be used in a classifier of fish otoliths, it is compared to two other Feature spaces, one with raw data and another with transformed data (using the Elliptic Fourier Descriptors EFD). Otoliths are found in the inner ear of fish. Their shape can be analyzed to determine sex, age, populations and species, and thus they can provide necessary and relevant information for ecological studies. The Automatic Taxon Identifier (ATI) is used to classify fish otoliths directly from a query image and is implemented on-line in a Public Database. This new Features space proposed in this study will allow the use of query images that are not normalized or are normalized in a different pose than the images in the Database. The rotation-invariant Feature space is the pre-processing part of a classifier based in kNearestNeighbours, and it is derived from the Elliptical Fourier Descriptors (EFD). The new Feature space compacts the information, and could be used when a feature reduction strategy is recommended. The authors analyze the classification results in a Test Database applying a controlled rotation to the Query images, and discuss their possible utility of the technique in the future ATI system.
