Similar image retrieval using Autoencoder. I. Automatic morphology classification of galaxies

TL;DR

This paper introduces TSGICAS, an autoencoder-based image retrieval system that efficiently classifies galaxy morphologies by comparing latent features, outperforming traditional methods in accuracy and speed.

Contribution

The paper presents a novel autoencoder-based approach for galaxy image retrieval and classification, demonstrating high accuracy across multiple galaxy catalogs.

Findings

Correlation coefficients of 0.735, 0.811, and 0.815 for different catalogs.

High visual similarity between retrieved and input galaxy images.

Efficient classification enabling rapid morphological analysis.

Abstract

We present the construction of an image similarity retrieval engine for the morphological classification of galaxies using the Convolutional AutoEncoder (CAE). The CAE is trained on 90,370 preprocessed Sloan Digital Sky Survey galaxy images listed in the Galaxy Zoo 2 (GZ2) catalog. The visually similar output images returned by the trained CAE suggest that the encoder efficiently compresses input images into latent features, which are then used to calculate similarity parameters. Our Tool for Searching a similar Galaxy Image based on a Convolutional Autoencoder using Similarity (TSGICAS) leverages this similarity parameter to classify galaxies' morphological types, enabling the identification of a wider range of classes with high accuracy compared to traditional supervised ML techniques. This approach streamlines the researcher's work by allowing quick prioritization of the most relevant images from the latent feature database. We investigate the accuracy of our automatic morphological classifications using three galaxy catalogs: GZ2, Extraction de Formes Id\'ealis\'ees de Galaxies en Imagerie (EFIGI), and Nair $\&$ Abraham (NA10). The correlation coefficients between the morphological types of input and retrieved galaxy images were found to be 0.735, 0.811, and 0.815 for GZ2, EFIGI, and NA10 catalogs, respectively. Despite differences in morphology tags between input and retrieved galaxy images, visual inspection showed that the two galaxies were very similar, highlighting TSGICAS's superior performance in image similarity search. We propose that morphological classifications of galaxies using TSGICAS are fast and efficient, making it a valuable tool for detailed galaxy morphological classifications in other imaging surveys.