A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
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A Visual Attention Model for Tracking Regions Based on Color Correlograms
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Bottom-Up Visual Attention for Virtual Human Animation
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Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
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International Journal of Computer Vision
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Efficient spatiotemporal-attention-driven shot matching
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Salient region detection by modeling distributions of color and orientation
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Video saliency detection with robust temporal alignment and local-global spatial contrast
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Saliency detection using joint spatial-color constraint and multi-scale segmentation
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Salient object detection via color contrast and color distribution
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Color boosted visual saliency detection and its application to image classification
Multimedia Tools and Applications
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Human vision system actively seeks interesting regions in images to reduce the search effort in tasks, such as object detection and recognition. Similarly, prominent actions in video sequences are more likely to attract our first sight than their surrounding neighbors. In this paper, we propose a spatiotemporal video attention detection technique for detecting the attended regions that correspond to both interesting objects and actions in video sequences. Both spatial and temporal saliency maps are constructed and further fused in a dynamic fashion to produce the overall spatiotemporal attention model. In the temporal attention model, motion contrast is computed based on the planar motions (homography) between images, which is estimated by applying RANSAC on point correspondences in the scene. To compensate the non-uniformity of spatial distribution of interest-points, spanning areas of motion segments are incorporated in the motion contrast computation. In the spatial attention model, a fast method for computing pixel-level saliency maps has been developed using color histograms of images. A hierarchical spatial attention representation is established to reveal the interesting points in images as well as the interesting regions. Finally, a dynamic fusion technique is applied to combine both the temporal and spatial saliency maps, where temporal attention is dominant over the spatial model when large motion contrast exists, and vice versa. The proposed spatiotemporal attention framework has been applied on over 20 testing video sequences, and attended regions are detected to highlight interesting objects and motions present in the sequences with very high user satisfaction rate.