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Predicting Perceived Emotions in Animated GIFs with 3D Convolutional Neural Networks
Name
16.Chen-etal-ISM.pdf
Description
Accepted version
Size
118.8 KB
Format
Adobe PDF
Checksum (MD5)
ce5fd4e2fcb2a3434a4bce576fd52643
Author(s)
Chen, Weixuan
Picard, Rosalind W.
Date Issued
December 2016
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chen, Weixuan and Picard, Rosalind W. 2016. "Predicting Perceived Emotions in Animated GIFs with 3D Convolutional Neural Networks."
Version
Author's final manuscript
Abstract
© 2016 IEEE. Animated GIFs are widely used on the Internet to express emotions, but their automatic analysis is largely unexplored before. To help with the search and recommendation of GIFs, we aim to predict their emotions perceived by humans based on their contents. Since previous solutions to this problem only utilize image-based features and lose all the motion information, we propose to use 3D convolutional neural networks (CNNs) to extract spatiotemporal features from GIFs. We evaluate our methodology on a crowd-sourcing platform called GIFGIF with more than 6000 animated GIFs, and achieve a better accuracy then any previous approach in predicting crowd-sourced intensity scores of 17 emotions. It is also found that our trained model can be used to distinguish and cluster emotions in terms of valence and risk perception.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/ism.2016.0081
