Highlight Detection in Movie Scenes Through Inter-users, Physiological Linkage

Chênes, Christophe; Chanel, Guillaume; Soleymani, Mohammad; Pun, Thierry

doi:10.1007/978-1-4471-4555-4_10

Cited by 27 publications

(26 citation statements)

References 53 publications

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“…Many studies have focused on matching spectators' physiological signals with affective states and the appearance of highlights in movies [5]. Since physiological reactions are considered to be an important component of emotions [15], [4], their measurements provide insight into spectators' aesthetic experience elicited by particular scenes [24].…”

Section: Introductionmentioning

confidence: 99%

“…In the field of affective computing, researchers have attempted to investigate emotion recognition in responses to multimedia content using electroencephalography (EEG) signals, peripheral physiological signals and facial expressions [14], [22]. The combination of spectators' physiological signals has been proposed in [5]. Because the spectators were watching separately a movie without any social context, they could not interact among themselves as it is the case in our studies.…”

Section: Introductionmentioning

confidence: 99%

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Synchronization among Groups of Spectators for Highlight Detection in Movies

Muszyński

Κωστούλας

Lombardo

et al. 2016

Proceedings of the 24th ACM International Conference on Multimedia

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Detection of emotional and aesthetic highlights is a challenge for the affective understanding of movies. Our assumption is that synchronized spectators' physiological and behavioral reactions occur during these highlights. We propose to employ the periodicity score to capture synchronization among groups of spectators' signals. To uncover the periodicity score's capabilities, we compare it with baseline synchronization measures, such as the nonlinear interdependence and the windowed mutual information. The results show that the periodicity score and the pairwise synchronization measures are able to capture different properties of spectators' synchronization, and they indicate the presence of some types of emotional and aesthetic highlights in a movie based on spectators' electro-dermal and acceleration signals.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synchronization among Groups of Spectators for Highlight Detection in Movies

Muszyński

Κωστούλας

Lombardo

et al. 2016

Proceedings of the 24th ACM International Conference on Multimedia

Self Cite

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show abstract

“…The users' behavior and spontaneous reactions to multimedia data can provide useful information for multimedia indexing with the following scenarios: (i) direct assessment of tags: users spontaneous reactions will be translated into emotional keywords, e.g., funny, disgusting, scary [18,13,16,19]; (ii) assessing the relevance of explicit tags or topic relevance, e.g., agreement or disagreement over a displayed tag or the relevance of the retrieved result [8,12,20,21]; (iii) user profiling: a user's personal preferences can be detected based on her reactions to retrieved data and be used for re-ranking the results; (iv) content summarization: highlight detection is also possible using implicit feedbacks from the users [22,23].…”

Section: Introductionmentioning

confidence: 99%

Multimedia implicit tagging using EEG signals

Soleymani

Pantić

2013

2013 IEEE International Conference on Multimedia and Expo (ICME)

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Electroencephalogram (EEG) signals reflect brain activities associated with emotional and cognitive processes. In this paper, we demonstrate how they can be used to find tags for multimedia content without users' direct input. Alternative methods for multimedia tagging is attracting increasing interest from multimedia community. The new portable EEG helmets are paving the way for employing brain waves in human computer interaction. In this paper, we demonstrate the performance of EEG for tagging purposes using two different scenarios on MAHNOB-HCI database. First, an emotional tagging and classification using a reduced set of electrodes is presented. The emotional responses of 24 participants to short video clips are classified into three classes on arousal and valence. We show how a reduced set of electrodes based on previous studies can preserve and even enhance the emotional classification rate. We then demonstrate the feasibility of using EEG signals for tag relevance tasks. A set of images were shown to participants first, without any tag and then with a relevant or irrelevant tag. The relevance of the tag was assessed based on the EEG responses of the participants in the first second after the tag was depicted. Finally, we demonstrate that by aggregating multiple participants' responses we can significantly improve the tagging accuracy.

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“…The cinematic technique exploits the high-level semantics in soccer game videos to detect the goal events, where the detection is based on four semantic rules including 30 ∼ 120 seconds of break duration, occurrence of close-up/out of field shot, existence of slow-motion replay, and the relative position of the replay shot. Note that although proposed ten years ago, this technique is still considered as state-of-the-art for highlighting the soccer game videos [17,18].…”

Section: Methodology and Metricsmentioning

confidence: 99%

Extracting viewer interests for automated bookmarking in video-on-demand services

Zhao

Tian

Liu

2014

Front. Comput. Sci.

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Video-on-demand (VoD) services have become popular on the Internet in recent years. In VoD, it is challenging to support the VCR functionality, especially the jumps, while maintaining a smooth streaming quality. Previous studies propose to solve this problem by predicting the jump target locations and prefetching the contents. However, through our analysis on traces from a real-world VoD service, we find that it would be fundamentally difficult to improve a viewer's VCR experience by simply predicting his future jumps, while ignoring the intentions behind these jumps.Instead of the prediction-based approach, in this paper, we seek to support the VCR functionality by bookmarking the videos. There are two key techniques in our proposed methodology. First, we infer and differentiate viewers' intentions in VCR jumps by decomposing the interseek times, using an expectation-maximization (EM) algorithm, and combine the decomposed inter-seek times with the VCR jumps to compute a numerical interest score for each video segment. Second, based on the interest scores, we propose an automated video bookmarking algorithm. The algorithm employs the time-series change detection techniques of CUSUM and MB-GT, and bookmarks videos by detecting the abrupt changes on their interest score sequences. We evaluate our proposed techniques using real-world VoD traces from dozens of videos. Experimental results suggest that with our methods, viewers' interests within a video can be precisely extracted, and we can position bookmarks on the video's highlight events accurately. Our proposed video bookmarking methodology does not require any knowledge on video type, contents, and semantics, and can be applied on various types of videos.

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Highlight Detection in Movie Scenes Through Inter-users, Physiological Linkage

Cited by 27 publications

References 53 publications

Synchronization among Groups of Spectators for Highlight Detection in Movies

Synchronization among Groups of Spectators for Highlight Detection in Movies

Multimedia implicit tagging using EEG signals

Extracting viewer interests for automated bookmarking in video-on-demand services

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