Abstract
Visual attention is an important issue in image and video analysis and keeps being an open problem in the computer vision field. Motivated by the famous Helmholtz principle, a new approach of visual attention analysis is proposed in this paper based on the low level feature statistics of natural images and the Bayesian framework. Firstly, two priors, i.e., Surrounding Feature Prior (SFP) and Single Feature Probability Distribution (SFPD) are learned and integrated by a Bayesian framework to compute the chance of happening (CoH) of each pixel in an image. Then another prior, i.e., Center Bias Prior (CBP), is learned and applied to the CoH to compute the saliency map of the image. The experimental results demonstrate that the proposed approach is both effective and efficient by providing more accurate and quick visual attention location. We make three major contributions in this paper: (1) A set of simple but powerful priors, SFP, SFPD and CBP, are presented in an intuitive way; (2) A computational model of CoH based on Bayesian framework is given to integrate SFP and SFPD together; (3) A computationally plausible way to obtain the saliency map of natural images based on CoH and CBP.
| Original language | English (US) |
|---|---|
| Title of host publication | Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings |
| Pages | 631-643 |
| Number of pages | 13 |
| Volume | 6315 LNCS |
| Edition | PART 5 |
| DOIs | |
| State | Published - 2010 |
| Externally published | Yes |
| Event | 11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece Duration: Sep 5 2010 → Sep 11 2010 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 5 |
| Volume | 6315 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 11th European Conference on Computer Vision, ECCV 2010 |
|---|---|
| Country | Greece |
| City | Heraklion, Crete |
| Period | 9/5/10 → 9/11/10 |
Fingerprint
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
What is the chance of happening : A new way to predict where people look. / Yang, Yezhou; Song, Mingli; Li, Na; Bu, Jiajun; Chen, Chun.
Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings. Vol. 6315 LNCS PART 5. ed. 2010. p. 631-643 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6315 LNCS, No. PART 5).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - What is the chance of happening
T2 - A new way to predict where people look
AU - Yang, Yezhou
AU - Song, Mingli
AU - Li, Na
AU - Bu, Jiajun
AU - Chen, Chun
PY - 2010
Y1 - 2010
N2 - Visual attention is an important issue in image and video analysis and keeps being an open problem in the computer vision field. Motivated by the famous Helmholtz principle, a new approach of visual attention analysis is proposed in this paper based on the low level feature statistics of natural images and the Bayesian framework. Firstly, two priors, i.e., Surrounding Feature Prior (SFP) and Single Feature Probability Distribution (SFPD) are learned and integrated by a Bayesian framework to compute the chance of happening (CoH) of each pixel in an image. Then another prior, i.e., Center Bias Prior (CBP), is learned and applied to the CoH to compute the saliency map of the image. The experimental results demonstrate that the proposed approach is both effective and efficient by providing more accurate and quick visual attention location. We make three major contributions in this paper: (1) A set of simple but powerful priors, SFP, SFPD and CBP, are presented in an intuitive way; (2) A computational model of CoH based on Bayesian framework is given to integrate SFP and SFPD together; (3) A computationally plausible way to obtain the saliency map of natural images based on CoH and CBP.
AB - Visual attention is an important issue in image and video analysis and keeps being an open problem in the computer vision field. Motivated by the famous Helmholtz principle, a new approach of visual attention analysis is proposed in this paper based on the low level feature statistics of natural images and the Bayesian framework. Firstly, two priors, i.e., Surrounding Feature Prior (SFP) and Single Feature Probability Distribution (SFPD) are learned and integrated by a Bayesian framework to compute the chance of happening (CoH) of each pixel in an image. Then another prior, i.e., Center Bias Prior (CBP), is learned and applied to the CoH to compute the saliency map of the image. The experimental results demonstrate that the proposed approach is both effective and efficient by providing more accurate and quick visual attention location. We make three major contributions in this paper: (1) A set of simple but powerful priors, SFP, SFPD and CBP, are presented in an intuitive way; (2) A computational model of CoH based on Bayesian framework is given to integrate SFP and SFPD together; (3) A computationally plausible way to obtain the saliency map of natural images based on CoH and CBP.
UR - http://www.scopus.com/inward/record.url?scp=78149312625&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149312625&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15555-0_46
DO - 10.1007/978-3-642-15555-0_46
M3 - Conference contribution
AN - SCOPUS:78149312625
SN - 3642155545
SN - 9783642155543
VL - 6315 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 631
EP - 643
BT - Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
ER -