EEG-video emotion-based summarization: Learning with EEG auxiliary signals
Publication Type
Journal Article
Publication Date
9-2022
Abstract
Video summarization is the process of selecting a subset of informative keyframes to expedite storytelling with limited loss of information. In this paper, we propose an EEG-Video Emotion-based Summarization (EVES) model based on a multimodal deep reinforcement learning (DRL) architecture that leverages neural signals to learn visual interestingness to produce quantitatively and qualitatively better video summaries. As such, EVES does not learn from the expensive human annotations but the multimodal signals. Furthermore, to ensure the temporal alignment and minimize the modality gap between the visual and EEG modalities, we introduce a Time Synchronization Module (TSM) that uses an attention mechanism to transform the EEG representations onto the visual representation space. We evaluate the performance of EVES on the TVSum and SumMe datasets. Based on the rank order statistics benchmarks, the experimental results show that EVES outperforms the unsupervised models and narrows the performance gap with supervised models. Furthermore, the human evaluation scores show that EVES receives a higher rating than the state-of-the-art DRL model DR-DSN by 11.4% on the coherency of the content and 7.4% on the emotion-evoking content. Thus, our work demonstrates the potential of EVES in selecting interesting content that is both coherent and emotion-evoking.
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Affective Computing
First Page
1
Last Page
13
ISSN
1949-3045
Identifier
10.1109/TAFFC.2022.3208259
Publisher
Institute of Electrical and Electronics Engineers
Citation
LEW, Wai-Cheong L.; WANG, Di; ANG, Kai-Keng; LIM, Joo-Hwee; QUEK, Chai; and TAN, Ah-hwee.
EEG-video emotion-based summarization: Learning with EEG auxiliary signals. (2022). IEEE Transactions on Affective Computing. 1-13.
Available at: https://ink.library.smu.edu.sg/sis_research/7567
Additional URL
http://doi.org/10.1109/TAFFC.2022.3208259