Name
Title | ||
|---|---|---|
An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval. |
Abstract | ||
|---|---|---|
Hashing methods have recently attracted great attention for approximate nearest neighbor search in massive remote sensing (RS) image archives due to their computational and storage effectiveness. The existing hashing methods in RS represent each image with a single-hash code that is usually obtained by applying hash functions to global image representations. Such an approach may not optimally repr... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LGRS.2018.2870686 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Semantics,Kernel,Remote sensing,Image retrieval,Clustering algorithms,Training,Approximation algorithms | Kernel (linear algebra),Approximation algorithm,Remote sensing,Image retrieval,Hash function,Cluster analysis,Nearest neighbor search,Mathematics,Semantics,Scalability | Journal |
Volume | Issue | ISSN |
16 | 2 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Reato | 1 | 0 | 0.34 |
| Begüm Demir | 2 | 339 | 30.36 |
| Lorenzo Bruzzone | 3 | 4952 | 387.72 |