A visual attention model for news video

In this paper, a novel method is proposed to perform saliency detection in news video. This method comprises bottom-up attention model which considers low level features to produce bottom-up saliency map and top-down attention model which utilizes high level factors to generate top-down saliency map. In bottom-up attention model, color image is represented as quaternion. Then the quaternion discrete cosine transform is used to detect static saliency in multi-scale and two color spaces. Meanwhile, the multi-scale local and global motion conspicuity maps are computed. To suppress the background motion noise, a novel histogram of average optical flow is proposed to calculate motion contrast. Then, the static saliency map and motion saliency map are fused after normalization. In top-down attention model, we explore high level factors of news video and generate the top-down saliency map based on these factors. Finally, the bottom-up and top-down saliency maps are integrated after normalization. Experiment results show that our method outperforms several state-of-the-art methods in saliency detection of news videos.