Emotion Classification of Film and Television Scene Images for Audiences

The purpose of the research on emotion classification of film and television (TV) scene images is to hope that the computer can simulate the audience's emotional perception to judge positive or negative emotional trends of the image. In this paper, from the perspective of emotional semantics, a method of the emotion classification about the film and TV scene image is proposed. Firstly, the film and TV scene image dataset were established and assessed based on the subjective evaluation experiment. Then the theory of psychology and photographic art was used to extract image emotional features. Finally, particle swarm optimization (PSO)-support vector machine (SVM) algorithm based on feature importance dimension reduction was used for building the classifier. The experimental results show that the image dataset initially established in this paper can be helpful to the subsequent study on emotion classification of the film and TV scene image, and the accuracy of the positive and negative emotion classifier on this dataset is 0.87, which conforms to audience's emotional perception of the film and TV scene image.