Empirical analysis of deep learning networks for affective video tagging
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
http://link.springer.com/content/pdf/10.1007/s11042-020-08714-y.pdf
Reference24 articles.
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2. Atkinson J, Campos D (2015) Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst Appl 47(2016):35–41, ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2015.10.049
3. Gupta R, ur Rehman Laghari K, Falk TH (2016) Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing 174(Part B):875–884, ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2015.09.085
4. Jirayucharoensak S, Pan-ngum S, Israsena P (2014) EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. TheScientificWorldJournal. 2014:627892. https://doi.org/10.1155/2014/627892
5. Kierkels JJM, Soleymani M, Pun T (2009) Queries and tags in affect-based multimedia retrieval. in Proc. Int. Conf. Multimedia and Expo. New York, NY, USA: IEEE Press, pp. 1436–1439
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