Dynamic saliency detection via CNN and spatial-temporal fusion
Qi Zhang, Dong Xu · 2018
Visual saliency prediction has obtained a significant popularity these years but the majority research is for static saliency prediction. An approach to detect dynamic saliency of videos is proposed in this paper, which exploits a spatial-temporal fusion way. Spatial saliency is detected by a trained convolutional neutral network, and we use a larger convolutional kernel for some layers in our network because saliency is influenced by global contrast according to visual psychology. While temporal saliency is extracted by optical flow and we combine it with K-means cluster, which brings a more accurate result. In addition, the two are fused in an optimal weighted way. Our experiments on DIEM datasets outperforms compared to four other dynamic saliency models on two metrics.