Name
Title | ||
|---|---|---|
Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification |
Abstract | ||
|---|---|---|
Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval. Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in the most previous methods, neglecting the significance level of d... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIP.2015.2467315 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Training,Image retrieval,Convolutional codes,Neural networks,Approximation methods,Optimization,Convolution | Locality-sensitive hashing,Computer vision,Pattern recognition,Double hashing,Computer science,Universal hashing,Feature hashing,Artificial intelligence,Dynamic perfect hashing,Hash table,Open addressing,Linear hashing | Journal |
Volume | Issue | ISSN |
24 | 12 | 1057-7149 |
Citations | PageRank | References |
188 | 4.05 | 33 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruimao Zhang | 1 | 325 | 18.86 |
| Liang Lin | 2 | 3007 | 151.07 |
| Rui Zhang | 3 | 270 | 19.86 |
| Wangmeng Zuo | 4 | 3833 | 173.11 |
| Lei Zhang | 5 | 16326 | 543.99 |