Research on Emotion Classification of Film and Television Scene Images Based on Different Salient Region

With the rapid development of intelligent image processing and computer vision, interpreting and extracting emotion information from images have become a hot research topic, especially the studies on film and television scene images with rich expressions and certain feelings from the aspect of audience. To investigate the distribution of emotions and its affect on the result of machine learning, this paper established the emotion classification models with different salient and non-salient regions based on self-built dataset from film and television works. Three salient extraction algorithms were used to the regional segmentation, and the color and texture features of each region were extracted. Emotion classification models were established based on SVM and RF algorithms respectively. Since features of the salient region could not cover all the information of the original image, the prediction accuracy of the unprocessed image was the highest. Nevertheless, when models made an error in the emotion prediction of the original image, the experimental results of salient region could get the correct answer under the certain circumstances. Through the analysis, the areas of interest can be used for emotion prediction to reduce the redundancy; however, to make it more accurate, the appropriate salient region extraction algorithm should be selected for different types of images and features. Our study provides data and theoretical guidance for image understanding and retrieval, and it has practical significance for more detailed classification based on the affective sensation.