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- W4387299730 abstract "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." @default.
- W4387299730 created "2023-10-04" @default.
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- W4387299730 date "2023-08-19" @default.
- W4387299730 modified "2023-10-14" @default.
- W4387299730 title "Deep supervised hashing for fast retrieval of radio image cubes" @default.
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- W4387299730 doi "https://doi.org/10.23919/ursigass57860.2023.10265687" @default.
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