@inproceedings{438c84311aa04053900ea4f16f9121d1,
title = "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.",
keywords = "Fixation duration, Memory bias, Scanpath prediction, Semantic features",
author = "Xuan Shao and Ye Luo and Dandan Zhu and Shuqin Li and Laurent Itti and Jianwei Lu",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70090-8\_1",
language = "英语",
isbn = "9783319700892",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "3--13",
editor = "Derong Liu and Shengli Xie and El-Alfy, \{El-Sayed M.\} and Dongbin Zhao and Yuanqing Li",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "德国",
}