A saliency map based on sampling an image into random rectangular regions of interest

In this article we propose a novel approach to compute an image saliency map based on computing local saliencies over random rectangular regions of interest. Unlike many of the existing methods, the proposed approach does not require any training bases, operates on the image at the original scale and has only a single parameter which requires tuning. It has been tested on the two distinct tasks of salient region detection (using MSRA dataset) and eye gaze prediction (using York University and MIT datasets). The proposed method achieves state-of-the-art performance on the eye gaze prediction task as compared with nine other state-of-the-art methods.

► A novel saliency map is proposed which is simple to implement.
► The saliency map is generated by random sampling of image into patches.
► The method is tested on the MSRA dataset for this task.
► It achieves state-of-the-art performance on the York dataset and also tested on the MIT dataset.