Efficient random saliency map detection

Most image retargeting algorithms rely heavily on valid saliency map detection to proceed. However, the inefficiency of high quality saliency map detection severely restricts the application of these image retargeting methods. In this paper, we propose a random algorithm for efficient context-aware saliency map detection. Our method is a multiple level saliency map detection algorithm that integrates multiple level coarse saliency maps into the resulting saliency map and selectively updates unreliable regions of the saliency map to refine detection results. Because of the randomized search, our method requires very little additional memory beyond that for the input image and result map, and does not need to build auxiliary data structures to accelerate the saliency map detection. We have implemented our algorithm on a GPU and demonstrated the performance for a variety of images and video sequences, compared with state-of-the-art image processing.