Affective content detection using HMMs
Hang-Bong Kang
- pp 259-262
TLDR
A new technique for detecting affective events using Hidden Markov Models with good accuracy is discussed, to map low level features of video data to high level emotional events.Abstract:
This paper discusses a new technique for detecting affective events using Hidden Markov Models(HMM). To map low level features of video data to high level emotional events, we perform empirical study on the relationship between emotional events and low-level features. After that, we compute simple low-level features that represent emotional characteristics and construct a token or observation vector by combining low level features. The observation vector sequence is tested to detect emotional events through HMMs. We create two HMM topologies and test both topologies. The affective events are detected from our proposed models with good accuracy.read more
Citations
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Video summarisation: A conceptual framework and survey of the state of the art
Arthur G. Money,Harry Agius +1 more
TL;DR: It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users.
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Affective understanding in film
Hee Lin Wang,Loong-Fah Cheong +1 more
TL;DR: A systematic approach grounded upon psychology and cinematography is developed to address several important issues in affective understanding and a holistic method of extracting affective information from the multifaceted audio stream has been introduced.
Proceedings ArticleDOI
Exploring Principles-of-Art Features For Image Emotion Recognition
TL;DR: Experiments demonstrate the superiority of PAEF for affective image classification and regression (with about 5% improvement on classification accuracy and 0.2 decrease in mean squared error), as compared to the state-of-the-art approaches.
Journal ArticleDOI
LIRIS-ACCEDE: A Video Database for Affective Content Analysis
TL;DR: A large video database, namely LIRIS-ACCEDE, is proposed, which consists of 9,800 good quality video excerpts with a large content diversity and provides four experimental protocols and a baseline for prediction of emotions using a large set of both visual and audio features.
Journal ArticleDOI
Extracting moods from pictures and sounds: towards truly personalized TV
TL;DR: The high potential of the affective video content analysis for enhancing the content recommendation functionalities of the future PVRs and VOD systems is shown.
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