TY - GEN
T1 - Deep supervised hashing for fast retrieval of radio image cubes
AU - Ndung’u Machetho, Stephen
AU - Grobler, Trienko L.
AU - Wijnholds, Stefan J.
AU - Karastoyanova, Dimka
AU - Azzopardi, George
N1 - Publisher Copyright:
© 2023 International Union of Radio Science.
PY - 2023/10/3
Y1 - 2023/10/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85174148738&partnerID=8YFLogxK
U2 - 10.23919/URSIGASS57860.2023.10265687
DO - 10.23919/URSIGASS57860.2023.10265687
M3 - Conference contribution
AN - SCOPUS:85174148738
T3 - 2023 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023
BT - Proceedings of the 35th URSI General Assembly and Scientific Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023
Y2 - 19 August 2023 through 26 August 2023
ER -