TY - JOUR
T1 - Towards learning emotional subspace
AU - Ko, Tobey H.
AU - Gu, Zhonglei
AU - He, Tiantian
AU - LIU, Yang
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503317, in part by the General Research Fund (GRF) from the Research Grant Council (RGC) of Hong Kong SAR under Project HKBU12202417, and in part by the SZSTI Grant with the Projct Code JCYJ20170307161544087.
PY - 2018
Y1 - 2018
N2 - We introduce a model designed to predict emotional impact of movies through affective video content analysis. Specifically, our approach utilizes a two-stage learning framework, which first conducts subspace learning using emotion preserving embedding (EPE) or biased discriminant embedding (BDE) to uncover the informative subspace from the original feature space according to the continuous or discrete emotional labels, respectively, and then carries out the prediction utilizing the support vector machine (SVM). Experimentation on a movie dataset validates the effectiveness of our learning framework. Copyright held by the owner/author(s).
AB - We introduce a model designed to predict emotional impact of movies through affective video content analysis. Specifically, our approach utilizes a two-stage learning framework, which first conducts subspace learning using emotion preserving embedding (EPE) or biased discriminant embedding (BDE) to uncover the informative subspace from the original feature space according to the continuous or discrete emotional labels, respectively, and then carries out the prediction utilizing the support vector machine (SVM). Experimentation on a movie dataset validates the effectiveness of our learning framework. Copyright held by the owner/author(s).
UR - http://www.scopus.com/inward/record.url?scp=85059864235&partnerID=8YFLogxK
UR - http://ceur-ws.org/Vol-2283/
M3 - Conference article
AN - SCOPUS:85059864235
VL - 2283
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
T2 - 2018 Working Notes Proceedings of the MediaEval Workshop, MediaEval 2018
Y2 - 29 October 2018 through 31 October 2018
ER -