Name
Title | ||
|---|---|---|
Low-Rank Latent Pattern Approximation with Applications to Robust Image Classification. |
Abstract | ||
|---|---|---|
This paper develops a novel method to address the structural noise in samples for image classification. Recently, regression-related classification methods have shown promising results when facing the pixelwise noise. However, they become weak in coping with the structural noise due to ignoring of relationships between pixels of noise image. Meanwhile, most of them need to implement the iterative ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIP.2017.2738560 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Image reconstruction,Testing,Robustness,Training,Measurement,Feature extraction,Lighting | Robustness (computer science),Artificial intelligence,Overfitting,Contextual image classification,Standard test image,Iterative reconstruction,Computer vision,Pattern recognition,Algorithm,Feature extraction,Matrix norm,Pixel,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 11 | 1057-7149 |
Citations | PageRank | References |
4 | 0.38 | 39 |
Authors | ||
6 | ||