Galaxies image classification using empirical mode decomposition and machine learning techniques

This article presents an automatic approach for galaxies images classification based on artificial neural network and empirical mode decomposition (EMD) algorithms. The proposed approach is consisted of two phases; namely feature extraction, and classification phases. For the feature extraction phase, (EMD) algorithm is applied to reduce the dimensionality of the feature space during the feature extraction phase. Finally, several machine learning classifiers were utilized for classifying the input galaxies images into one of four obtained source catalogue types including multi-Layer preception, generalized feed-forward, and recurrent networks. Experimental results showed that multi-Layer preception provided better classification results in conjunction with the empirical mode decomposition. It is also concluded that a small set of features is sufficient to classify galaxy images and provide a fast classification. Keywords: Hubble Sequence, Artificial Neural Network (ANN), Mean Squared Error (MSE), Multi-Layer Preception (MLP), Generalized Feed-Forward(GFF), Recurrent Network( RN).