Deep supervised hashing for fast retrieval of radio image cubes
Steven Ndung'u Trienko Grobler Stefan J. Wijnholds Dimka Karastoyanova George Azzopardi
The shear number of sources that will be detected by next-generation radio
surveys will be astronomical, which will result in serendipitous discoveries.
Data-dependent deep hashing algorithms have been shown to be efficient at image
retrieval tasks in the fields of computer vision and multimedia. However, there
are limited applications of these methodologies in the field of astronomy. In
this work, we utilize deep hashing to rapidly search for similar images in a
large database. The experiment uses a balanced dataset of 2708 samples
consisting of four classes: Compact, FRI, FRII, and Bent. The performance of
the method was evaluated using the mean average precision (mAP) metric where a
precision of 88.5\% was achieved. The experimental results demonstrate the
capability to search and retrieve similar radio images efficiently and at
scale. The retrieval is based on the Hamming distance between the binary hash
of the query image and those of the reference images in the database.