Name
Abstract | ||
|---|---|---|
In this paper, an automatic method for extracting visual saliency based on selective integration of feature maps in frequency domain is proposed. Feature maps are calculated by measuring the Bayes spectral entropy. In order to extract visual saliency effectively, feature maps are first generated from three images separated into Y, Cb, Cr channels, respectively. Then, by selectively integrating feature maps, visual saliency is finally extracted. Experimental results have shown that the proposed method obtains good performance of visual saliency under various environments containing multiple objects and cluttered backgrounds in natural images. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICCE.2013.6486787 | ICCE |
Keywords | Field | DocType |
computer vision,entropy,feature extraction,bayes spectral entropy,feature map integration,frequency domain,image separation,natural image,visual saliency extraction | Frequency domain,Computer vision,Kadir–Brady saliency detector,Pattern recognition,Feature (computer vision),Computer science,Feature extraction,Spectral entropy,Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Visual saliency,Bayes' theorem | Conference |
ISSN | ISBN | Citations |
2158-3994 | 978-1-4673-1361-2 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ki Tae Park | 1 | 24 | 6.23 |
| Jeong Ho Lee | 2 | 4 | 2.14 |
| Young Shik Moon | 3 | 110 | 16.82 |