Salient region detection and its application to video retargeting
In spite of extensive studies on visual saliency, e.g., generating a saliency map from an image, less work has been addressed how to crop salient regions from saliency maps. We present a new approach to detect salient regions with maximum saliency density from videos. A branch-and-bound search algorithm is developed to find the global optimal solution efficiently. The proposed detection approach can automatically adapt to the shapes and motions of salient objects regardless of cluttered backgrounds. Moreover, by introducing an intermediate cropping window, video retargeting as an application of salient region detection gets optimized saliency coverage. Extensive experimental results validate the advantages of the proposed method.