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Visual Tracking by Local Superpixel Matching with Markov Random Field Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Video Affective Content Analysis Based on Protagonist via Convolutional Neural Network

Authors : Yingying Zhu, Zhengbo Jiang, Jianfeng Peng, Sheng-hua Zhong

Published in: Advances in Multimedia Information Processing - PCM 2016

Publisher: Springer International Publishing

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Abstract

Affective recognition is an important and challenging task for video content analysis. Affective information in videos is closely related to the viewer’s feelings and emotions. Thus, video affective content analysis has a great potential value. However, most of the previous methods are focused on how to effectively extract features from videos for affective analysis. There are several issues are worth to be investigated. For example, what information is used to express emotions in videos, and which information is useful to affect audiences’ emotions. Taking into account these issues, in this paper, we proposed a new video affective content analysis method based on protagonist information via Convolutional Neural Network (CNN). The proposed method is evaluated on the largest video emotion dataset and compared with some previous work. The experimental results show that our proposed affective analysis method based on protagonist information achieves best performance in emotion classification and prediction.

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previous chapter Leveraging Composition of Object Regions for Aesthetic Assessment of Photographs
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Metadata
Title
Video Affective Content Analysis Based on Protagonist via Convolutional Neural Network
Authors
Yingying Zhu
Zhengbo Jiang
Jianfeng Peng
Sheng-hua Zhong
Copyright Year
2016
Book
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48889-9

Electronic ISBN: 978-3-319-48890-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48890-5_17