面向大规模遥感图像搜索的局部保持多视图图哈希
Locality Preserving Multiview Graph Hashing for Large Scale Remote Sensing Image Search
AI总结：
本文提出一种局部保持多视图图哈希方法，通过在低维子空间学习共识紧凑码并引入超参数可学习模块，有效解决了大规模遥感图像检索问题，实验证明其性能具竞争力。
摘要：
哈希在遥感图像搜索中非常流行。本文提出一种具有可学习参数的多视图哈希方法，用于大规模遥感数据集的查询图像检索。现有方法常忽略真实世界遥感数据位于嵌入高维环境空间中的低维流形上。与以往方法不同，本文提出在特定视图的低维子空间中学习共识紧凑码。此外，我们添加了超参数可学习模块以避免复杂的参数调优。为证明方法的有效性，我们在三个广泛使用的遥感数据集上进行了实验，并与七种最先进的方法进行了比较。大量实验表明，所提方法能取得具有竞争力的结果。
英文摘要：
Hashing is very popular for remote sensing image search. This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote sensing dataset. Existing methods always neglect that real-world remote sensing data lies on a low-dimensional manifold embedded in high-dimensional ambient space. Unlike previous methods, this article proposes to learn the consensus compact codes in a view-specific low-dimensional subspace. Furthermore, we have added a hyperparameter learnable module to avoid complex parameter tuning. In order to prove the effectiveness of our method, we carried out experiments on three widely used remote sensing data sets and compared them with seven state-of-the-art methods. Extensive experiments show that the proposed method can achieve competitive results compared to the other method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
