An Experience Oriented Video Digesting Method Using Heart Activity and Its Applicable Video Types

Toyosawa, Satoshi; Kawai, Takashi

doi:10.1007/978-3-642-15702-8_24

Cited by 2 publications

(2 citation statements)

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“…Their experiments on a database of six films viewed by 16 participants showed high correlation between subjects' physiological peaks and emotional tags of videos, and the catalysis of music-rich segments in stimulating viewer response. Toyosawa and Kawai [105] proposed heart rates for video digesting. They determined the attention level of each segment through deceleration of heart rate and the high frequency component of heart rate variability.…”

Section: Implicit Video Affective Content Analysis Using Physiologicamentioning

confidence: 99%

“…Although the studies described above explored physiological signals to analyze the affective content of a video, their purposes (i.e., summarization [26], [105], retrieval [91], highlight detection [96], and tagging [104], [106]) are not the same. In addition, they used different methods for emotion dimension prediction, such as linear relevance vector machine [91], and emotion category classification, such as decision tree [13], Gaussian process classifiers [97] and SVM [96], [106] etc.…”

Section: Implicit Video Affective Content Analysis Using Physiologicamentioning

confidence: 99%

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Video Affective Content Analysis: A Survey of State-of-the-Art Methods

Wang

2015

IEEE Trans. Affective Comput.

163

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Video affective content analysis has been an active research area in recent decades, since emotion is an important component in the classification and retrieval of videos. Video affective content analysis can be divided into two approaches: direct and implicit. Direct approaches infer the affective content of videos directly from related audiovisual features. Implicit approaches, on the other hand, detect affective content from videos based on an automatic analysis of a user's spontaneous response while consuming the videos. This paper first proposes a general framework for video affective content analysis, which includes video content, emotional descriptors, and users' spontaneous nonverbal responses, as well as the relationships between the three. Then, we survey current research in both direct and implicit video affective content analysis, with a focus on direct video affective content analysis. Lastly, we identify several challenges in this field and put forward recommendations for future research.Index Terms-Video affective content analysis, emotion recognition, and content-based video retrieval.

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Section: Implicit Video Affective Content Analysis Using Physiologicamentioning

confidence: 99%