Authors:
AbdulWahab Kabani
and
Mahmoud R. El-Sakka
Affiliation:
The University of Western Ontario, Canada
Keyword(s):
Galaxy Classification, Image Classification, Deep Neural Networks, Convolutional Neural Networks, Machine Learning, Computer Vision, Image Processing, Data Analysis.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
In this paper, we study the importance of scale on Galaxy Image Classification. Galaxy Image classification involves performing Morphological Analysis to determine the shape of the galaxy. Traditionally, Morphological Analysis is carried out by trained experts. However, as the number of images of galaxies is increasing, there’s a desire to come up with a more scalable approach for classification. In this paper, we pre-process the images to have three different scales. Then, we train the same neural network for small number of epochs (number of passes over the data) on all of these three scales. After that, we report the performance of the neural network on each scale. There are two main contributions in this paper. First, we show that scale plays a major role in the performance of the neural network. Second, we show that normalizing the scale of the galaxy image produces better results. Such normalization can be extended to any image classification task with similar characteristics t
o the galaxy images and where there’s no background clutter.
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