Scanpath Prediction Based on High-Level Features and Memory Bias

Abstract

Human scanpath prediction aims to use computational models to mimic human gaze shifts under free view conditions. Previous works utilizing low-level features, hand-crafted high-level features, saccadic amplitude, memory bias cannot fully explain the mechanism of visual attention. In this paper, we propose a comprehensive method to predict scanpath from four aspects: low-level features, saccadic amplitude, semantic features learned via deep convolutional neural network, memory bias including short-term and long-term memory. By calculating the probabilities for all candidate regions in an image, the position of next fixation point can be selected via picking the one with the largest probability product. Moreover, fixation duration as a key factor is first used to model memory effect on scanpath prediction. Experiments on two public datasets demonstrate the effectiveness of the proposed method, and comparisons with state-of-the-art methods further validate the superiority of our method.