A supervised contrastive learning-based model for image emotion classification

Images play a vital role in social media platforms, which can more vividly reflect people's inner emotions and preferences, so visual sentiment analysis has become an important research topic. In this paper, we propose a Supervised Contrastive Learning-based model for image emotion classification, which consists of two modules of low-level feature extraction and deep emotional feature extraction, and feature fusion is used to enhance the overall perception of image emotions. In the low-level feature extraction module, the LBP-U (Local Binary Patterns with Uniform Patterns) algorithm is employed to extract texture features from the images, which can effectively capture the texture information of the images, aiding in the differentiation of images belonging to different emotion categories. In the deep emotional feature extraction module, we introduce a Supervised Contrastive Learning approach to improve the extraction of deep emotional features by narrowing the intra-class distance among images of the same emotion category while expanding the inter-class distance between images of different emotion categories. Through fusing the low-level and deep emotional features, our model comprehensively utilizes features at different levels, thereby enhancing the overall emotion classification performance. To assess the classification performance and generalization capability of the proposed model, we conduct experiments on the publicly FI (Flickr and Instagram) Emotion dataset. Comparative analysis of the experimental results demonstrates that our proposed model has good performance for image emotion classification. Additionally, we conduct ablation experiments to analyze the impact of different levels of features and various loss functions on the model's performance, thereby validating the superiority of our proposed approach.