A family of shape ellipticity measures for galaxy classification

Abstract

A new family of shape ellipticity measures is introduced. Each measure from the family distinguishes among the ellipses with different axis length ratios. This is not case for existing ellipticity measures. The new measures are theoretically well founded, which helps us to better understand their behavior and suitability to certain applications. All measures from the family are invariant with respect to translation, rotation, and scaling transformations; range over (0,1]; and pick the value 1 only for the ellipses with a specific ratio between minor and major axis lengths. The measures from the new family are applied to the galaxy classification tasks. The 100% classification rate was achieved by using a k-NN classifier. Only six parameters (feature vector components), computed from three different ellipticity measures, were used. Based on 100 mutually independent experiments, an average classification rate of 95.6% was obtained. Previously accuracies of 92.3% and 95.1% were obtained using nearest neighbor and neural network classifiers, respectively.