Deep intrinsic image popularity assessment by learning to rank

The importance of popularity in assessing the value of visual content is particularly notable in contemporary social media contexts. The demand for optimizing image set compression based on projected popularity has surged. However, the relationship between intrinsic visual content and its influence on popularity is not yet fully understood. In our study, a new approach is introduced called the Intrinsic Image Popularity Assessment (IIPA) scheme, which leverages deep neural networks (DNNs) to predict popularity. The IIPA model is trained using a learning-to-rank approach. Due to the lack of a pre-existing IIPA dataset, a probabilistic method is developed to automatically generate millions of image pairs with discernible popularity differences, creating the first large-scale IIPA database. Comprehensive testing demonstrates that our proposed IIPA model surpasses current methods and achieves human-level performance.