Suggesting the Appropriate Number of Observers for Predicting Video Saliency with Eye-Tracking Data
Chuancai Li, Jiayi Xu, Jianjun Li, Xiaoyang Mao · 2018
Accurately predicting video saliency is important for applications such as video quality assessment, summary, compression, and retargeting. As the automatic saliency models for videos suffer from problems of inaccuracy, determining video saliency from data on the human gaze is a promising approach. Due to differences in individual observers, however, eye-tracking data of a certain number of observers are usually required to compute a visual attention map close to the ground truth. Although it has become cheaper to acquire human eye-tracking data thanks to the lower price of equipment, it is still not easy to carry out studies with a large number of observers. To keep the balance between accuracy and expense, this paper proposes a new method for suggesting the appropriate number of observers needed in eye-tracking experiments for a given video. Through carefully analyzing eye-tracking data of various video clips, we found videos can be classified into four types based on the number of observers required to approach the ground truth. A new support vector machine (SVM) classifier was trained to automatically classify videos into one of the four typical types.