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Fine-grained emotion prediction for movie and television scene images
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NETL
NSTL
万方数据
For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the image.In addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional emotions.The experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image dataset.This study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.
fine-grained emotion predictionmovie and television scene imagesstacking modellinear regression
Su Zhibin、Zhou Xuanye、Liu Bing、Ren Hui
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State Key Laboratory of Media Convergence and Communication,Communication University of China,Beijing 100024,China
Key Laboratory of Acoustic Visual Technology and Intelligent Control System,Communication University of China,Beijing 100024,China
School of Information and Communication Engineering,Communication University of China,Beijing 100024,China
Open Project of Key Laboratory of Audio and Video Restoration and Evaluation
NETL
NSTL
万方数据
