Name
Title | ||
|---|---|---|
Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning. |
Abstract | ||
|---|---|---|
Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary “mask” map that can identify the approximate locations of objects in an image, so that we use this binary “mask” map to obtain length-limited hash codes ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2018.2839522 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Training,Integrated circuits,Databases,Training data,Encyclopedias,Electronic publishing | Cross entropy,Computer vision,Data set,Ranking,Pattern recognition,Feature (computer vision),Image retrieval,Artificial intelligence,Hash function,Nearest neighbor search,Mathematics,Binary number | Journal |
Volume | Issue | ISSN |
27 | 9 | 1057-7149 |
Citations | PageRank | References |
4 | 0.40 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changqin Huang | 1 | 77 | 9.54 |
| Shang-Ming Yang | 2 | 4 | 0.40 |
| Yan Pan | 3 | 179 | 19.23 |
| Hanjiang Lai | 4 | 234 | 17.67 |