Name
Title | ||
|---|---|---|
General Distributed Hash Learning on Image Descriptors for $k$-Nearest Neighbor Search. |
Abstract | ||
|---|---|---|
Hashing methods have attracted much attention due to their superior time and storage properties for image retrieval. To learn similarity-preserving hash function, most existing methods are designed for the centralized setting. However, the current data storage systems are distributed to increase scalability. Obviously, it is infeasible to aggregate all the data into a fusion center because of the ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2019.2907777 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Hash functions,Hamming distance,Computational complexity,Binary codes,Training,Distributed databases,Computational modeling | k-nearest neighbors algorithm,Distributed element model,Pattern recognition,Computer data storage,Image retrieval,Theoretical computer science,Artificial intelligence,Fusion center,Hash function,Mathematics,Scalability,Computation | Journal |
Volume | Issue | ISSN |
26 | 5 | 1070-9908 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||