Name
Abstract | ||
|---|---|---|
Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questio... |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TIP.2016.2514503 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Kernel,Visualization,Support vector machines,Additives,Histograms,Shape,Image recognition | Feature selection,Computer science,Visual learning,Artificial intelligence,Classifier (linguistics),Discriminative model,Computer vision,Pattern recognition,Visualization,Multiple kernel learning,Support vector machine,Machine learning,Visual Word | Journal |
Volume | Issue | ISSN |
25 | 3 | 1057-7149 |
Citations | PageRank | References |
3 | 0.37 | 36 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji Zhao 0001 | 1 | 117 | 8.66 |
| Liantao Wang | 2 | 3 | 0.37 |
| Ricardo Silveira Cabral | 3 | 3 | 0.37 |
| Fernando De La Torre | 4 | 3832 | 181.17 |