Name
Abstract | ||
|---|---|---|
By the guidance of attention, human visual system is able to locate objects of interest in complex scene. In this paper, we propose a novel visual saliency detection method - the conditional saliency for both image and video. Inspired by biological vision, the definition of visual saliency follows a strictly local approach. Given the surrounding area, the saliency is defined as the minimum uncertainty of the local region, namely the minimum conditional entropy, when the perceptional distortion is considered. To simplify the problem, we approximate the conditional entropy by the lossy coding length of multivariate Gaussian data. The final saliency map is accumulated by pixels and further segmented to detect the proto-objects. Experiments are conducted on both image and video. And the results indicate a robust and reliable feature invariance saliency. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-12307-8_23 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
reliable feature invariance saliency,local approach,visual saliency,novel visual saliency detection,conditional saliency,minimum conditional entropy,conditional entropy,local region,human visual system,final saliency map | Computer vision,Kadir–Brady saliency detector,Pattern recognition,Invariant (physics),Computer science,Human visual system model,Salience (neuroscience),Multivariate normal distribution,Artificial intelligence,Pixel,Conditional entropy,Distortion | Conference |
Volume | Issue | ISSN |
5994 LNCS | PART 1 | 16113349 |
ISBN | Citations | PageRank |
3-642-12306-6 | 38 | 1.27 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Li | 1 | 797 | 35.85 |
| Yue Zhou | 2 | 176 | 11.68 |
| Junchi Yan | 3 | 891 | 83.36 |
| Zhibin Niu | 4 | 72 | 7.55 |
| Jie Yang | 5 | 1392 | 157.55 |