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Title: Discovering salient objects from videos using spatiotemporal salient region detection
Authors: Kannan, R
Ghinea, G
Swaminathan, S
Keywords: Salient region detection;Temporal saliency;Optical flow abstraction;Spatiotemporal saliency detection;Saliency map
Issue Date: 2015
Publisher: Elsevier
Citation: Signal Processing: Image Communication, 36: 154 - 178, ( 2015)
Abstract: Detecting salient objects from images and videos has many useful applications in computer vision. In this paper, a novel spatiotemporal salient region detection approach is proposed. The proposed approach computes spatiotemporal saliency by estimating spatial and temporal saliencies separately. The spatial saliency of an image is computed by estimating the color contrast cue and color distribution cue. The estimations of these cues exploit the patch level and region level image abstractions in a unified way. The aforementioned cues are fused to compute an initial spatial saliency map, which is further refined to emphasize saliencies of objects uniformly, and to suppress saliencies of background noises. The final spatial saliency map is computed by integrating the refined saliency map with center prior map. The temporal saliency is computed based on local and global temporal saliencies estimations using patch level optical flow abstractions. Both local and global temporal saliencies are fused to compute the temporal saliency. Finally, spatial and temporal saliencies are integrated to generate a spatiotemporal saliency map. The proposed temporal and spatiotemporal salient region detection approaches are extensively experimented on challenging salient object detection video datasets. The experimental results show that the proposed approaches achieve an improved performance than several state-of-the-art saliency detection approaches. In order to compensate different needs in respect of the speed/accuracy tradeoff, faster variants of the spatial, temporal and spatiotemporal salient region detection approaches are also presented in this paper.
ISSN: 0923-5965
Appears in Collections:Dept of Computer Science Research Papers

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