Video emotion recognition based on Convolutional Neural Networks - Details

Author：

Li, Chen (Li, Chen.) | Shi, Yuliang (Shi, Yuliang.) | Yi, Xianjin (Yi, Xianjin.)

Indexed by：

EI Scopus

Abstract：

The　existing　video　sentiment　analysis　methods　only　obtain　features　from　the　spatial　and　temporal　signals　of　the　video　for　sentiment　classification,　and　cannot　solve　the　difficulty　of　not　knowing　which　emotion　contributes　the　most　to　the　entire　video　sentiment　analysis　in　the　video　sentiment　analysis.　To　solve　this　problem,　a　neural　network　with　video　frame　weight　vector　is　proposed.　First,　the　video　frame　feature　is　obtained　through　the　reel　neural　network,　and　then　the　weight　vector　layer　is　used　to　calculate　the　weight　of　the　feature,　and　finally　the　frame　feature　with　weight　is　put　into　the　LSTM　Training　to　obtain　a　video　sentiment　analysis　model.　We　verified　on　the　BAUM-1s　data　set.　The　results　show　that　this　method　is　better　than　existing　methods　in　accuracy.　©　2020　Published　under　licence　by　IOP　Publishing　Ltd.

Keyword：

Long short-term memory Computer networks Sentiment analysis Convolutional neural networks Multilayer neural networks

Author Community：

[ 1 ] [Li, Chen]Faculty of Information Technology, Beijing University of Technology, Beijing; 100123, China
[ 2 ] [Shi, Yuliang]Faculty of Information Technology, Beijing University of Technology, Beijing; 100123, China
[ 3 ] [Yi, Xianjin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100123, China