Name
Abstract | ||
|---|---|---|
Recent works have demonstrated that image descriptors produced by convolutional feature maps provide state-of-the-art performance for image retrieval and classification problems. However, features from a single convolutional layer are not robust enough for shape deformation, scale variation, and heavy occlusion. In this letter, we present a simple and straightforward approach for extracting multis... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LSP.2017.2665522 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Image retrieval,Feature extraction,Convolution,Convolutional codes,Signal processing algorithms,Robustness,Neural networks | Convolutional code,Pattern recognition,Feature detection (computer vision),Convolution,Convolutional neural network,Computer science,Image retrieval,Robustness (computer science),Feature extraction,Artificial intelligence,Artificial neural network | Journal |
Volume | Issue | ISSN |
24 | 5 | 1070-9908 |
Citations | PageRank | References |
6 | 0.41 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Li | 1 | 35 | 9.77 |
| yulong xu | 2 | 31 | 2.66 |
| Jiabao Wang | 3 | 22 | 11.31 |
| Zhuang Miao | 4 | 23 | 7.51 |
| Yafei Zhang | 5 | 14 | 1.57 |