用于射电图像立方体快速检索的深度监督哈希
Deep supervised hashing for fast retrieval of radio image cubes
AI总结：
本研究将深度监督哈希应用于射电图像立方体的快速检索，在2708个样本的四类平衡数据集上达到88.5%的mAP，验证了该方法在天文领域大规模高效检索的可行性。
摘要：
下一代射电巡天将探测到的源数量将是天文数字，这将带来偶然发现。数据相关的深度哈希算法已被证明在计算机视觉和多媒体领域的图像检索任务中高效。然而，这些方法在天文学领域的应用有限。在本工作中，我们利用深度哈希在大型数据库中快速搜索相似图像。实验使用了一个包含四类（Compact、FRI、FRII和Bent）的2708个样本的平衡数据集。使用平均精度均值（mAP）指标评估方法性能，达到了88.5%的精度。实验结果表明，该方法能够高效且大规模地搜索和检索相似的射电图像。检索基于查询图像二进制哈希与数据库中参考图像二进制哈希之间的汉明距离。
英文摘要：
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.
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