Affective Video Content Analysis: A Multidisciplinary Insight

Baveye, Yoann; Chamaret, Christel; Dellandréa, Emmanuel; Chen, Liming

doi:10.1109/taffc.2017.2661284

Cited by 67 publications

(52 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It remains a challenging task where only limited performance has been achieved for predicting induced emotions [7]. Here we briefly review the state-of-the-art of affective content analysis.…”

Section: Related Workmentioning

confidence: 99%

“…However, Baveye et. al [7] argued that emotions intended by the directors may not always be consistent with emotions induced in the audience, although they did not discuss perceived emotions. To the best of our knowledge, there has been no previous work formally addressing the relationship between perceived and induced emotions of movie audiences.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?

Tian

Muszyński

Lai

et al. 2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)

View full text Add to dashboard Cite

Abstract-Predicting the emotional response of movie audiences to affective movie content is a challenging task in affective computing. Previous work has focused on using audiovisual movie content to predict movie induced emotions. However, the relationship between the audience's perceptions of the affective movie content (perceived emotions) and the emotions evoked in the audience (induced emotions) remains unexplored. In this work, we address the relationship between perceived and induced emotions in movies, and identify features and modelling approaches effective for predicting movie induced emotions. First, we extend the LIRIS-ACCEDE database by annotating perceived emotions in a crowd-sourced manner, and find that perceived and induced emotions are not always consistent. Second, we show that dialogue events and aesthetic highlights are effective predictors of movie induced emotions. In addition to movie based features, we also study physiological and behavioural measurements of audiences. Our experiments show that induced emotion recognition can benefit from including temporal context and from including multimodal information. Our study bridges the gap between affective content analysis and induced emotion prediction.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?

Tian

Muszyński

Lai

et al. 2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)

View full text Add to dashboard Cite

show abstract

“…In this paper, we focus on approaches belonging to the first way that represents affective dimensions using features extracted from the data. For a recent survey on AVCA, readers are referred to [12], [13].…”

Section: B Avca Approaches Using Affective Dimensionsmentioning

confidence: 99%

Synchronous prediction of arousal and valence using LSTM network for affective video content analysis

Zhang

2017

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

View full text Add to dashboard Cite

The affect embedded in video data conveys high-level semantic information about the content and has direct impact on the understanding and perception of reviewers, as well as their emotional responses. Affective Video Content Analysis (AVCA) attempts to generate a direct mapping between video content and the corresponding affective states such as arousal and valence dimensions. Most existing studies establish the mapping for each dimension separately using knowledge-based rules or traditional classifiers such as Support Vector Machine (SVM). The inherent correlations between affective dimensions have largely been unexploited, which are anticipated to include important information for accurate prediction of affective dimensions. To address this issue, this paper presents an approach to predict arousal and valance dimensions synchronously using the Long Short Term Memory (LSTM) network. The approach extracts a set of low-level audio and visual features from video data and projects them synchronously into pairs of arousal and valence values using the LSTM network which automatically incorporates the correlations between arousal and valance dimensions. We evaluate the performance of the proposed approach on a dataset comprising video clips segmented from real-world resources such as film, drama, and news, and demonstrate its superior performance over the traditional SVM based method. The results provide one of the earliest preliminary evidence to the benefit of considering correlations between affective dimensions towards accurate AVCA.

show abstract

“…Nevertheless, Baveye et. al [10] argued that emotions intended by the directors might not always be consistent with emotions that are induced in movie audiences. In fact, the authors did not consider perceived emotions in their studies.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Induced Emotions of Movie Audiences from Multimodal Information

Muszyński

Tian

Lai

et al. 2021

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

Recognizing emotional reactions of movie audiences to affective movie content is a challenging task in affective computing. Previous research on induced emotion recognition has mainly focused on using audiovisual movie content. Nevertheless, the relationship between the perceptions of the affective movie content (perceived emotions) and the emotions evoked in the audiences (induced emotions) is unexplored. In this work, we studied the relationship between perceived and induced emotions of movie audiences. Moreover, we investigated multimodal modelling approaches to predict movie induced emotions from movie content based features, as well as physiological and behavioral reactions of movie audiences. To carry out analysis of induced and perceived emotions, we first extended an existing database for movie affect analysis by annotating perceived emotions in a crowd-sourced manner. We find that perceived and induced emotions are not always consistent with each other. In addition, we show that perceived emotions, movie dialogues, and aesthetic highlights are discriminative for movie induced emotion recognition besides spectators' physiological and behavioral reactions. Also, our experiments revealed that induced emotion recognition could benefit from including temporal information and performing multimodal fusion. Moreover, our work deeply investigated the gap between affective content analysis and induced emotion recognition by gaining insight into the relationships between aesthetic highlights, induced emotions, and perceived emotions.

show abstract

Affective Video Content Analysis: A Multidisciplinary Insight

Cited by 67 publications

References 87 publications

Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?

Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?

Synchronous prediction of arousal and valence using LSTM network for affective video content analysis

Recognizing Induced Emotions of Movie Audiences from Multimodal Information

Contact Info

Product

Resources

About