Locality Preserving Multiview Graph Hashing for Large Scale Remote Sensing Image Search
This addresses efficient image retrieval for remote sensing applications, though it appears incremental as it builds on existing multiview hashing approaches.
The authors tackled the problem of large-scale remote sensing image search by proposing a multiview hashing method that learns consensus compact codes in view-specific low-dimensional subspaces, achieving competitive results compared to seven state-of-the-art methods on three datasets.
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.