基于区域协方差的视频显著度局部空时优化模型

doi:10.11999/JEIT151122

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Abstract

Visual saliency is widely applied to computer vision. Image saliency detection has been extensively studied, while there are only a few effective methods of computing saliency for videos owing to its high challenge. Inspired by image saliency methods, this paper proposes a unified spatiotemporal feature extraction and optimization framework for video saliency. First, the spatiotemporal feature descriptor is constructed via region covariance. Then, initial saliency map is computed by the local contrast of the descriptor. Finally, a local spatiotemporal optimization framework considering the previous and next frames of the current one is modeled to obtain the final saliency map. Extensive experiments on two public datasets demonstrate that the proposed algorithm not only outperforms the state-of-the-art methods, but also is of great extendibility.

Key words： Video saliency Region covariance Local contrast Local spatiotemporal optimization

Received: 08 October 2015 Published: 07 April 2016

PACS:

TP391.41

Fund:

The National Natural Science Youth Foundation of China (61501509)

Corresponding Authors: JIANG Qingzhu E-mail: jqbamboo@126.com

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	TIAN Chang
	JIANG Qingzhu
	WU Zemin
	LIU Tao
	HU Lei

Cite this article:

TIAN Chang,JIANG Qingzhu,WU Zemin等. A Local Spatiotemporal Optimization Framework for Video Saliency Detection Using Region Covariance[J]. JEIT, 2016, 38(7): 1586-1593.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT151122 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I7/1586

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