Learning-based prediction of visual attention for video signals
- PMID: 21518666
- DOI: 10.1109/TIP.2011.2144610
Learning-based prediction of visual attention for video signals
Abstract
Visual attention, which is an important characteristic of human visual system, is a useful clue for image processing and compression applications in the real world. This paper proposes a computational scheme that adopts both low-level and high-level features to predict visual attention from video signal by machine learning. The adoption of low-level features (color, orientation, and motion) is based on the study of visual cells, and the adoption of the human face as a high-level feature is based on the study of media communications. We show that such a scheme is more robust than those using purely single low- or high-level features. Unlike conventional techniques, our scheme is able to learn the relationship between features and visual attention to avoid perceptual mismatch between the estimated salience and the actual human fixation. We also show that selecting the representative training samples according to the fixation distribution improves the efficacy of regressive training. Experimental results are shown to demonstrate the advantages of the proposed scheme.
Similar articles
-
A coherent computational approach to model bottom-up visual attention.IEEE Trans Pattern Anal Mach Intell. 2006 May;28(5):802-17. doi: 10.1109/TPAMI.2006.86. IEEE Trans Pattern Anal Mach Intell. 2006. PMID: 16640265 Clinical Trial.
-
Fast and robust generation of feature maps for region-based visual attention.IEEE Trans Image Process. 2008 May;17(5):633-44. doi: 10.1109/TIP.2008.919365. IEEE Trans Image Process. 2008. PMID: 18390370
-
Three-dimensional face pose detection and tracking using monocular videos: tool and application.IEEE Trans Syst Man Cybern B Cybern. 2009 Aug;39(4):935-44. doi: 10.1109/TSMCB.2008.2009566. Epub 2009 Mar 24. IEEE Trans Syst Man Cybern B Cybern. 2009. PMID: 19336335
-
Predicting visual fixations on video based on low-level visual features.Vision Res. 2007 Sep;47(19):2483-98. doi: 10.1016/j.visres.2007.06.015. Epub 2007 Aug 3. Vision Res. 2007. PMID: 17688904
-
Generating customised experimental stimuli for visual search using Genetic Algorithms shows evidence for a continuum of search efficiency.Vision Res. 2009 Feb;49(3):374-82. doi: 10.1016/j.visres.2008.11.006. Epub 2008 Dec 23. Vision Res. 2009. PMID: 19059427
Cited by
-
Cluster-based co-saliency detection.IEEE Trans Image Process. 2013 Oct;22(10):3766-78. doi: 10.1109/TIP.2013.2260166. Epub 2013 Apr 25. IEEE Trans Image Process. 2013. PMID: 23629857 Free PMC article.
-
What do saliency models predict?J Vis. 2014 Mar 11;14(3):14. doi: 10.1167/14.3.14. J Vis. 2014. PMID: 24618107 Free PMC article.
-
Sex differences in a chronometric mental rotation test with cube figures: a behavioral, electroencephalography, and eye-tracking pilot study.Neuroreport. 2018 Jul 4;29(10):870-875. doi: 10.1097/WNR.0000000000001046. Neuroreport. 2018. PMID: 29738354 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
