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基于區(qū)域協(xié)方差的視頻顯著度局部空時優(yōu)化模型

田暢 姜青竹 吳澤民 劉濤 胡磊

田暢, 姜青竹, 吳澤民, 劉濤, 胡磊. 基于區(qū)域協(xié)方差的視頻顯著度局部空時優(yōu)化模型[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1586-1593. doi: 10.11999/JEIT151122
引用本文: 田暢, 姜青竹, 吳澤民, 劉濤, 胡磊. 基于區(qū)域協(xié)方差的視頻顯著度局部空時優(yōu)化模型[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1586-1593. doi: 10.11999/JEIT151122
TIAN Chang, JIANG Qingzhu, WU Zemin, LIU Tao, HU Lei. A Local Spatiotemporal Optimization Framework for Video Saliency Detection Using Region Covariance[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1586-1593. doi: 10.11999/JEIT151122
Citation: TIAN Chang, JIANG Qingzhu, WU Zemin, LIU Tao, HU Lei. A Local Spatiotemporal Optimization Framework for Video Saliency Detection Using Region Covariance[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1586-1593. doi: 10.11999/JEIT151122

基于區(qū)域協(xié)方差的視頻顯著度局部空時優(yōu)化模型

doi: 10.11999/JEIT151122
基金項(xiàng)目: 

國家自然科學(xué)基金青年基金(61501509)

A Local Spatiotemporal Optimization Framework for Video Saliency Detection Using Region Covariance

Funds: 

The National Natural Science Youth Foundation of China (61501509)

  • 摘要: 顯著度檢測在計(jì)算機(jī)視覺中應(yīng)用非常廣泛,圖像級的顯著度檢測研究已較為成熟,但視頻顯著度因其高度挑戰(zhàn)性研究相對較少。該文借鑒圖像級顯著度算法的思想,提出一種通用的空時特征提取與優(yōu)化模型來檢測視頻顯著度。首先利用區(qū)域協(xié)方差矩陣構(gòu)造視頻的空時特征描述子,然后計(jì)算對比度得出初始顯著圖,最后通過聯(lián)合前后幀的局部空時優(yōu)化模型得到最終的顯著圖。在2個公開視頻顯著性數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果表明,所提算法性能優(yōu)于目前的主流算法,同時具有良好的擴(kuò)展性。
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出版歷程
  • 收稿日期:  2015-10-08
  • 修回日期:  2016-02-29
  • 刊出日期:  2016-07-19

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