Name
Abstract | ||
|---|---|---|
Hashing methods have attracted much attention for large-scale image retrieval. Some deep hashing methods have achieved promising results by taking advantage of the strong representation power of deep networks recently. However, existing deep hashing methods treat all hash bits equally. On one hand, a large number of images share the same distance to a query image due to the discrete Hamming distan... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMM.2018.2804763 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Hamming distance,Image retrieval,Semantics,Multimedia communication,Training,Multimedia databases,Streaming media | Locality-sensitive hashing,Pattern recognition,Double hashing,Computer science,Universal hashing,Feature hashing,Artificial intelligence,Hash function,Machine learning,Dynamic perfect hashing,Linear hashing,Hash table | Journal |
Volume | Issue | ISSN |
20 | 9 | 1520-9210 |
Citations | PageRank | References |
7 | 0.39 | 32 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Zhang | 1 | 82 | 4.12 |
| Yuxin Peng | 2 | 1122 | 74.90 |