A Visual Saliency Map Based on Random Sub-window Means

In this article, we propose a simple and efficient method for computing an image saliency map, which performs well on both salient region detection and as well as eye gaze prediction tasks. A large number of distinct sub-windows with random co-ordinates and scales are generated over an image. The saliency descriptor of a pixel within a random sub-window is given by the absolute difference of its intensity value to the mean intensity of the sub-window. The final saliency value of a given pixel is obtained as the sum of all saliency descriptors corresponding to this pixel. Any given pixel can be included by one or more random subwindows. The recall-precision performance of the proposed saliency map is comparable to other existing saliency maps for the task of salient region detection. It also achieves state-of-the-art performance for the task of eye gaze prediction in terms of receiver operating characteristics.