Salient object detection based on eye tracking data

Abstract

Salient object detection aims to detect the region of interest in an image and provides an efficient solution for image semantic understanding. Eye tracking data can accurately track where human interested in an image, which has been introduced for image processing recently. In this paper, we propose a new saliency detection model with a combination of superpixel segmentation and eye tracking data. Eye tracking data are firstly introduced for reducing the number of superpixels and speeding up the calculation. And then, we give a strategy for training sets selection and construct a learning model to detect salient object. Finally, the feature contrast and multi-scale strategy are combined to propose an integrated model and get the refinement saliency map. We compare our method to the 10 state-of-the-art methods on the two public image datasets. These results show that our method outperforms existing methods and eye tracking technology is a highly promising tools for saliency object detection.