ISSN : 1796-2048
Volume : 3    Issue : 3    Date : July 2008

Integrated Feature Selection and Clustering for Taxonomic Problems within Fish Species
Huimin Chen, Henry L. Bart Jr., and Shuqing Huang
Page(s): 10-17
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As computer and database technologies advance rapidly, biologists all over the world can share
biologically meaningful data from images of specimens and use the data to classify the specimens
taxonomically. Accurate shape analysis of a specimen from multiple views of 2D images is crucial
for finding diagnostic features using geometric morphometric techniques. We propose an
integrated feature selection and clustering framework that automatically identifies a set of feature
variables to group specimens into a binary cluster tree. The candidate features are generated from
reconstructed 3D shape and local saliency characteristics from 2D images of the specimens. A
Gaussian mixture model is used to estimate the significance value of each feature and control the
false discovery rate in the feature selection process so that the clustering algorithm can efficiently
partition the specimen samples into clusters that may correspond to different species. The
experiments on a taxonomic problem involving species of suckers in the genus Carpiodes
demonstrate promising results using the proposed framework with only a small size of samples.

Index Terms
feature selection, clustering, taxonomy, shape analysis, false discovery rate, image fusion