Abstract | ||
---|---|---|
Saliency detection for images and videos has become increasingly popular due to its wide applicability. In this paper, we present a new method that takes advantage of region-based visual dynamic contrast to generate temporally coherent video saliency maps. The concept of visual dynamics is formulated to represent both visual and motional variabilities of video content. Moreover, the regions are regarded as primitives for saliency computation by using spatiotemporal appearance contrasts. Then, region matching is performed across successive video frames to form temporally coherent regions, which are computed on the basis of spatiotemporal similarity in the visual dynamics of the different regions along the optical flow in the video. The region matching can effectively eliminate saliency discontinuities, particularly in the areas of oversegmentation that are otherwise highly problematic. The proposed approach is tested on a challenging set of video sequences and is compared with contemporary methods to demonstrate its superior performance in terms of its computational efficiency and ability to detect salient video content. |
Year | DOI | Venue |
---|---|---|
2013 | 10.1109/TCSVT.2013.2270367 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | Field | DocType |
temporal coherence,temporally coherent regions,video signal processing,video frames,temporally coherent video saliency maps,image matching,video content motional variabilities,video sequences,regional dynamic contrast,salient video content detection,image segmentation,gabor filtering,videos saliency detection,oversegmentation,video content visual variabilities,image saliency detection,image sequences,region-based visual dynamic contrast,object detection,video saliency,spatiotemporal appearance contrasts,optical flow,temporally coherent video saliency,region matching,graphic processing unit (gpu)-based segmentation | Computer vision,Object detection,Pattern recognition,Salience (neuroscience),Computer science,Image segmentation,Contrast (statistics),Video tracking,Artificial intelligence,Optical flow,Computation,Salient | Journal |
Volume | Issue | ISSN |
23 | 12 | 1051-8215 |
Citations | PageRank | References |
9 | 0.45 | 21 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yong Li | 1 | 9 | 0.45 |
Bin Sheng | 2 | 368 | 61.19 |
Lizhuang Ma | 3 | 498 | 100.70 |
Wen Wu | 4 | 517 | 47.40 |
Zhifeng Xie | 5 | 53 | 10.70 |