IEEE Trans Image Process. 2014 Sep;23(9):3910-21. doi: 10.1109/TIP.2014.2336549. Epub 2014 Jul 16.
We propose a novel algorithm to detect visual saliency from video signals by combining both spatial and temporal information and statistical uncertainty measures. The main novelty of the proposed method is twofold. First, separate spatial and temporal saliency maps are generated, where the computation of temporal saliency incorporates a recent psychological study of human visual speed perception. Second, the spatial and temporal saliency maps are merged into one using a spatiotemporally adaptive entropy-based uncertainty weighting approach. The spatial uncertainty weighing incorporates the characteristics of proximity and continuity of spatial saliency, while the temporal uncertainty weighting takes into account the variations of background motion and local contrast. Experimental results show that the proposed spatiotemporal uncertainty weighting algorithm significantly outperforms state-of-the-art video saliency detection models.
我们提出了一种新的算法，通过结合空间和时间信息以及统计不确定性度量来从视频信号中检测视觉显著性。所提出方法的主要新颖之处有两点。首先，生成了单独的空间和时间显著图，其中时间显著度的计算包含了对人类视觉速度感知的最新心理学研究。其次，使用基于时空自适应熵的不确定性加权方法将空间和时间显著图合并为一个图。空间不确定性加权考虑了空间显著度的接近性和连续性的特征，而时间不确定性加权则考虑了背景运动和局部对比度的变化。实验结果表明，所提出的时空不确定性加权算法显著优于最新的视频显著度检测模型。
