Visual Sentiment Classification via Low-Rank Regularization and Label Relaxation

In the human cognitive system, the emotional feeling is a complicated process. Visual sentiment classification aims to predict the human emotions evoked by different images. In this article, we proposed a novel visual sentiment classification algorithm by modeling this task as a low-rank subspace learning problem. To reduce the discrepancy between global and local features, image features of relevant regions are selected from the whole image by sparse encoding. The label relaxation item is employed for alleviating the label ambiguity caused by subjective evaluation. We develop an alternative iterative method to optimize the proposed objective function. This model can be naturally extended for online learning, which improves efficiency. We conduct extensive experiments on three publicly available data sets. Compared with several state-of-the-art methods, we achieve better performance.