Video saliency detection based on robust seeds generation and spatio-temporal propagation

Kai Tian, Zongqing Lu, Q. Liao, Na Wang
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引用次数: 2

Abstract

This paper proposes a novel video saliency detection method for unconstrained videos with various motion patterns and complex scenes. We fuse multiple tempo-scale optical flow with discarding rule to enhance the reliability of motion information. Based on efficiently computation of motion distinction, our algorithm is able to locate the foreground and background approximately. Considering the mutuality of video frames, we regard video saliency seeds generation as the pattern mining process. With the help of robust saliency seeds, spatio-temporal propagation is performed in both intra-frame and inter-frame graphs. This provides an effective way to refine saliency maps. Quantitative and qualitative experiments are carried out on two benchmark video datasets, which show that our approach achieves state-of-the-art performance in video saliency detection.
基于鲁棒种子生成和时空传播的视频显著性检测
针对具有多种运动模式和复杂场景的无约束视频,提出了一种新的视频显著性检测方法。我们将多个时间尺度光流与丢弃规则融合在一起,以提高运动信息的可靠性。基于高效的运动区分计算,该算法能够对前景和背景进行近似定位。考虑到视频帧之间的相互关系,我们将视频显著性种子的生成视为模式挖掘过程。借助鲁棒显著性种子,在帧内和帧间图中进行时空传播。这提供了一种改进显著性图的有效方法。在两个基准视频数据集上进行了定量和定性实验,结果表明我们的方法在视频显著性检测方面达到了最先进的性能。
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