Research on emotion space of film and television scene images based on subjective perception
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摘要
The premise of image emotion recognition is to determine its representative emotional adjectives and establish the quantifiable emotion space. In this paper, focusing on aroused emotion from film and television(TV) scene images, a method of selecting emotional adjectives and establishing the emotion space based on subjective perception experiment is proposed. Firstly, a special data set about film and TV scene images was established and a set of initial emotional adjectives was collected. Then the subjective perception experiment was designed to let subjects to evaluate the affection of all the initial adjectives during watching these scene images. Then the method of principal basis analysis was used for variable selection. Finally, the factor analysis was applied to accomplish the second dimension reduction to form a 5-dimensional(5 D) orthogonal emotion space. The optimized emotion space can explain more than 94% of original emotional adjectives, which greatly reduces the dimension of emotional adjectives and lays a foundation for the further research on image content and emotion recognition.
The premise of image emotion recognition is to determine its representative emotional adjectives and establish the quantifiable emotion space. In this paper, focusing on aroused emotion from film and television(TV) scene images, a method of selecting emotional adjectives and establishing the emotion space based on subjective perception experiment is proposed. Firstly, a special data set about film and TV scene images was established and a set of initial emotional adjectives was collected. Then the subjective perception experiment was designed to let subjects to evaluate the affection of all the initial adjectives during watching these scene images. Then the method of principal basis analysis was used for variable selection. Finally, the factor analysis was applied to accomplish the second dimension reduction to form a 5-dimensional(5 D) orthogonal emotion space. The optimized emotion space can explain more than 94% of original emotional adjectives, which greatly reduces the dimension of emotional adjectives and lays a foundation for the further research on image content and emotion recognition.
The premise of image emotion recognition is to determine its representative emotional adjectives and establish the quantifiable emotion space. In this paper, focusing on aroused emotion from film and television(TV) scene images, a method of selecting emotional adjectives and establishing the emotion space based on subjective perception experiment is proposed. Firstly, a special data set about film and TV scene images was established and a set of initial emotional adjectives was collected. Then the subjective perception experiment was designed to let subjects to evaluate the affection of all the initial adjectives during watching these scene images. Then the method of principal basis analysis was used for variable selection. Finally, the factor analysis was applied to accomplish the second dimension reduction to form a 5-dimensional(5 D) orthogonal emotion space. The optimized emotion space can explain more than 94% of original emotional adjectives, which greatly reduces the dimension of emotional adjectives and lays a foundation for the further research on image content and emotion recognition.
引文
1.Tang L P,Chen F.Research progress of emotion-based image classification.Information Studies:Theory&Application,2018,41(6):149-160(in Chinese)
2.Zhao S C,Yao H Y.A survey of image emotion computing.Intelligent Computer and Applications,2017,7(1):1-5(in Chinese)
3.Ekman P.An argument for basic emotions.Cognition&Emotion,1992,6(3/4):169-200
4.Russell J A,Mehrabian A.Evidence for a three-factor theory of emotions.Journal of Research in Personality,1977,11(3):273-294
5.Li P T,Shi Y X,Dai H G.Classification of house-designing image based on color feature.Computer Engineering,2011,37(16):224-229(in Chinese)
6.Wang S F,Chen A H,Wang S,et al.A Kansei image retrieval based on emotion model.Journal of Circuits and Systems,2003,8(6):48-52(in Chinese)
7.Li H F,He H P,Chen J J.A multi-layer affective model based on personality,mood and emotion.Journal of Computer-Aided Design&Computer Graphics,2011,23(4):725-730(in Chinese)
8.WU X H.Research on emotional semantic image annotation and retrieval.Hangzhou,China:Zhejiang University,2013(in Chinese)
9.Cao J F,Chen J J,Li H F.On natural scene image classification based on improved OCC affective model.Computer Applications and Software,2014,31(6):181-184(in Chinese)
2.Zhao S C,Yao H Y.A survey of image emotion computing.Intelligent Computer and Applications,2017,7(1):1-5(in Chinese)
3.Ekman P.An argument for basic emotions.Cognition&Emotion,1992,6(3/4):169-200
4.Russell J A,Mehrabian A.Evidence for a three-factor theory of emotions.Journal of Research in Personality,1977,11(3):273-294
5.Li P T,Shi Y X,Dai H G.Classification of house-designing image based on color feature.Computer Engineering,2011,37(16):224-229(in Chinese)
6.Wang S F,Chen A H,Wang S,et al.A Kansei image retrieval based on emotion model.Journal of Circuits and Systems,2003,8(6):48-52(in Chinese)
7.Li H F,He H P,Chen J J.A multi-layer affective model based on personality,mood and emotion.Journal of Computer-Aided Design&Computer Graphics,2011,23(4):725-730(in Chinese)
8.WU X H.Research on emotional semantic image annotation and retrieval.Hangzhou,China:Zhejiang University,2013(in Chinese)
9.Cao J F,Chen J J,Li H F.On natural scene image classification based on improved OCC affective model.Computer Applications and Software,2014,31(6):181-184(in Chinese)