Name
Abstract | ||
|---|---|---|
Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to automatically label all images with different weather conditions (e.g., sunny, cloudy, and overcast), using limited human assistance. To address the drawbacks in existing weather prediction algorithms, we first apply image segmentation to the raw images to avoid disturbance of the non-sky region. Then, we propose to use multiple kernel learning to gather and select an optimal subset of image features from a certain feature pool. To further increase the recognition performance, we adopt multi-pass active learning for selecting the training set. The experimental results show that our weather recognition system achieves high performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICIP.2012.6467244 | ICIP |
Keywords | Field | DocType |
monitoring camera,weather recognition,visual information,monitoring webcams,learning (artificial intelligence),automatic inference,visual databases,image segmentation,multiple kernel learning,panorama images,active learning,image sensors,image retrieval,weather information retrieval,image dataset,learning artificial intelligence | Computer vision,Automatic image annotation,Pattern recognition,Feature detection (computer vision),Image texture,Feature (computer vision),Computer science,Multiple kernel learning,Image retrieval,Image segmentation,Artificial intelligence,Visual Word | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4673-2532-5 | 978-1-4673-2532-5 | 7 |
PageRank | References | Authors |
0.53 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zichong Chen | 1 | 21 | 2.93 |
| Feng Yang | 2 | 86 | 11.70 |
| Albrecht Lindner | 3 | 20 | 2.63 |
| Guillermo Barrenetxea | 4 | 414 | 27.80 |
| Martin Vetterli | 5 | 13926 | 2397.68 |