NDLI: Deep learning vs. kernel methods: Performance for emotion prediction in videos

Content Provider	IEEE Xplore Digital Library
Author	Baveye, Y. Dellandrea, E. Chamaret, C. Liming Chen
Copyright Year	2015
Description	Author affiliation: Technicolor, Cesson Sévigné, France (Baveye, Y.; Chamaret, C.) \|\| Ecole Centrale de Lyon, Univ. de Lyon, Lyon, France (Dellandrea, E.; Liming Chen)
Abstract	Recently, mainly due to the advances of deep learning, the performances in scene and object recognition have been progressing intensively. On the other hand, more subjective recognition tasks, such as emotion prediction, stagnate at moderate levels. In such context, is it possible to make affective computational models benefit from the breakthroughs in deep learning? This paper proposes to introduce the strength of deep learning in the context of emotion prediction in videos. The two main contributions are as follow: (i) a new dataset, composed of 30 movies under Creative Commons licenses, continuously annotated along the induced valence and arousal axes (publicly available) is introduced, for which (ii) the performance of the Convolutional Neural Networks (CNN) through supervised fine-tuning, the Support Vector Machines for Regression (SVR) and the combination of both (Transfer Learning) are computed and discussed. To the best of our knowledge, it is the first approach in the literature using CNNs to predict dimensional affective scores from videos. The experimental results show that the limited size of the dataset prevents the learning or finetuning of CNN-based frameworks but that transfer learning is a promising solution to improve the performance of affective movie content analysis frameworks as long as very large datasets annotated along affective dimensions are not available.
Starting Page	77
Ending Page	83
File Size	2088692
Page Count	7
File Format	PDF
ISSN	21568111
e-ISBN	9781479999538
DOI	10.1109/ACII.2015.7344554
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deep learning Affective computing Correlation Computational modeling Hidden Markov models Machine learning Motion pictures Benchmarking Kernel Videos Continuous emotion prediction
Content Type	Text
Resource Type	Article

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