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http://dx.doi.org/10.14372/IEMEK.2021.16.1.17

Development of Galaxy Image Classification Based on Hand-crafted Features and Machine Learning  

Oh, Yoonju (Kyungpook National University)
Jung, Heechul (Kyungpook National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.1, 2021 , pp. 17-27 More about this Journal
Abstract
In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.
Keywords
Galaxy image classification; Machine learning; Hand-crafted feature; Image processing; Support vector machine; Logistic regression; Random forest; ORB feature;
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