Hybrid video emotional tagging using users’ EEG and video content
Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from e...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Shangfei [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 72(2013), 2 vom: 10. Apr., Seite 1257-1283 |
|---|---|
| Übergeordnetes Werk: |
volume:72 ; year:2013 ; number:2 ; day:10 ; month:04 ; pages:1257-1283 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-013-1450-8 |
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OLC2035012406 |
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| 520 | |a Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. | ||
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| 700 | 1 | |a Ji, Qiang |4 aut | |
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10.1007/s11042-013-1450-8 doi (DE-627)OLC2035012406 (DE-He213)s11042-013-1450-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Shangfei verfasserin aut Hybrid video emotional tagging using users’ EEG and video content 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. Emotional tagging Videos Independent feature-level fusion Decision-level fusion Dependent feature-level fusion Zhu, Yachen aut Wu, Guobing aut Ji, Qiang aut Enthalten in Multimedia tools and applications Springer US, 1995 72(2013), 2 vom: 10. Apr., Seite 1257-1283 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:72 year:2013 number:2 day:10 month:04 pages:1257-1283 https://doi.org/10.1007/s11042-013-1450-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 72 2013 2 10 04 1257-1283 |
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10.1007/s11042-013-1450-8 doi (DE-627)OLC2035012406 (DE-He213)s11042-013-1450-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Shangfei verfasserin aut Hybrid video emotional tagging using users’ EEG and video content 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. Emotional tagging Videos Independent feature-level fusion Decision-level fusion Dependent feature-level fusion Zhu, Yachen aut Wu, Guobing aut Ji, Qiang aut Enthalten in Multimedia tools and applications Springer US, 1995 72(2013), 2 vom: 10. Apr., Seite 1257-1283 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:72 year:2013 number:2 day:10 month:04 pages:1257-1283 https://doi.org/10.1007/s11042-013-1450-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 72 2013 2 10 04 1257-1283 |
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10.1007/s11042-013-1450-8 doi (DE-627)OLC2035012406 (DE-He213)s11042-013-1450-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Shangfei verfasserin aut Hybrid video emotional tagging using users’ EEG and video content 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. Emotional tagging Videos Independent feature-level fusion Decision-level fusion Dependent feature-level fusion Zhu, Yachen aut Wu, Guobing aut Ji, Qiang aut Enthalten in Multimedia tools and applications Springer US, 1995 72(2013), 2 vom: 10. Apr., Seite 1257-1283 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:72 year:2013 number:2 day:10 month:04 pages:1257-1283 https://doi.org/10.1007/s11042-013-1450-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 72 2013 2 10 04 1257-1283 |
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10.1007/s11042-013-1450-8 doi (DE-627)OLC2035012406 (DE-He213)s11042-013-1450-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Shangfei verfasserin aut Hybrid video emotional tagging using users’ EEG and video content 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. Emotional tagging Videos Independent feature-level fusion Decision-level fusion Dependent feature-level fusion Zhu, Yachen aut Wu, Guobing aut Ji, Qiang aut Enthalten in Multimedia tools and applications Springer US, 1995 72(2013), 2 vom: 10. Apr., Seite 1257-1283 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:72 year:2013 number:2 day:10 month:04 pages:1257-1283 https://doi.org/10.1007/s11042-013-1450-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 72 2013 2 10 04 1257-1283 |
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10.1007/s11042-013-1450-8 doi (DE-627)OLC2035012406 (DE-He213)s11042-013-1450-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Shangfei verfasserin aut Hybrid video emotional tagging using users’ EEG and video content 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. Emotional tagging Videos Independent feature-level fusion Decision-level fusion Dependent feature-level fusion Zhu, Yachen aut Wu, Guobing aut Ji, Qiang aut Enthalten in Multimedia tools and applications Springer US, 1995 72(2013), 2 vom: 10. Apr., Seite 1257-1283 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:72 year:2013 number:2 day:10 month:04 pages:1257-1283 https://doi.org/10.1007/s11042-013-1450-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 72 2013 2 10 04 1257-1283 |
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Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. © Springer Science+Business Media New York 2013 |
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Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. © Springer Science+Business Media New York 2013 |
| abstract_unstemmed |
Abstract In this paper, we propose novel hybrid approaches to annotate videos in valence and arousal spaces by using users’ electroencephalogram (EEG) signals and video content. Firstly, several audio and visual features are extracted from video clips and five frequency features are extracted from each channel of the EEG signals. Secondly, statistical analyses are conducted to explore the relationships among emotional tags, EEG and video features. Thirdly, three Bayesian Networks are constructed to annotate videos by combining the video and EEG features at independent feature-level fusion, decision-level fusion and dependent feature-level fusion. In order to evaluate the effectiveness of our approaches, we designed and conducted the psychophysiological experiment to collect data, including emotion-induced video clips, users’ EEG responses while watching the selected video clips, and emotional video tags collected through participants’ self-report after watching each clip. The experimental results show that the proposed fusion methods outperform the conventional emotional tagging methods that use either video or EEG features alone in both valence and arousal spaces. Moreover, we can narrow down the semantic gap between the low-level video features and the users’ high-level emotional tags with the help of EEG features. © Springer Science+Business Media New York 2013 |
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Hybrid video emotional tagging using users’ EEG and video content |
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Zhu, Yachen Wu, Guobing Ji, Qiang |
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