Fusion of visual attention model and SVM for sentiment analysis of planar images

The sentiment analysis of images is actually a classification problem. However, a common classifier is unable to handle the emotion classification of images. This study proposes a planar image sentiment analysis recognition method that simulates the human eye recognition process to enhance the accuracy of sentiment analysis in planar images. This method utilises an improved block wise adaptive weighted colour histogram and a cognitive feature extraction model that integrates visual attention models to extract features from images. Finally, this method combines several extracted features of the image and uses support vector machines for sentiment recognition and classification. The results showed that when the words in the dictionary were 90, the algorithm performed the best, with the highest accuracy of 69%. In emotion recognition classification, the more image features were fused, the better the emotion classification recognition effect of the image. When integrating four features, the highest classification accuracy of the algorithm was 72.9%. The research proposes a planar image sentiment analysis method that integrates visual attention models and support vector machines, which can effectively simulate the changes in the focus area features of the human eye when observing images. This can achieve accurate recognition of emotions expressed in images.