Knowledge-Based Morphological Classification of Galaxies from Vision Features

Dhami, Devendra Singh (Indiana University Bloomington) | Leake, David (Indiana University Bloomington) | Natarajan, Sriraam (Indiana University Bloomington)

AAAI Conferences 

This paper presents a knowledge-based approach to the task of learning and identifying galaxies from their images. To this effect, we propose a crowd-sourced pipeline approach that employs two systems - case based and rule based systems. First, the approach extracts morphological features i.e. features describing the structure of the galaxy such as its shape, central characteristics e.g., has a bar or bulge at its center)etc., using computer vision techniques. Then it employs a case based reasoning system and a rule based system to perform the classification task. Our initial results show that this pipeline is effective in learning reasonably accurate models on this complex task.

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