Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting

We propose a novel algorithm to detect visual saliency from video signals by combining both spatial and temporal information and statistical uncertainty measures. The main novelty of the proposed method is twofold. First, separate spatial and temporal saliency maps are generated, where the computati...

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Bibliographic Details
Published inIEEE transactions on image processing Vol. 23; no. 9; pp. 3910 - 3921
Main Authors Fang, Yuming, Wang, Zhou, Lin, Weisi, Fang, Zhijun
Format Journal Article
LanguageEnglish
Published United States IEEE 01.09.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial Intelligence
Biological system modeling
Biomimetics - methods
Computational modeling
Computer Simulation
Feature extraction
Humans
Image color analysis
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Mathematical models
Models, Statistical
Pattern Recognition, Automated - methods
Perception
Photography - methods
Reproducibility of Results
Sensitivity and Specificity
Spatio-Temporal Analysis
Spatiotemporal phenomena
Subtraction Technique
Temporal logic
Uncertainty
Video Recording - methods
Visual
Visual Perception - physiology
Visualization
Weighting
video saliency
Visual attention
spatiotemporal saliency detection
uncertainty weighting
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Summary:We propose a novel algorithm to detect visual saliency from video signals by combining both spatial and temporal information and statistical uncertainty measures. The main novelty of the proposed method is twofold. First, separate spatial and temporal saliency maps are generated, where the computation of temporal saliency incorporates a recent psychological study of human visual speed perception. Second, the spatial and temporal saliency maps are merged into one using a spatiotemporally adaptive entropy-based uncertainty weighting approach. The spatial uncertainty weighing incorporates the characteristics of proximity and continuity of spatial saliency, while the temporal uncertainty weighting takes into account the variations of background motion and local contrast. Experimental results show that the proposed spatiotemporal uncertainty weighting algorithm significantly outperforms state-of-the-art video saliency detection models.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2014.2336549