Low-rank regularized multi-view inverse-covariance estimation for visual sentiment distribution prediction

Abstract With the increasing tendency of using images to express opinions and share experiences, sentiment analysis of visual content has aroused considerable attention interests in the past few years. Traditional sentiment analysis methods mainly focus on predicting the most dominant sentiment category of images while neglecting the sentiment ambiguity problem restricted by various factors such as environment, subjectivity, and cultural background. To tackle this problem, visual sentiment distribution prediction has been put forward to characterize images by distributions over a set of sentiment labels instead of a single distinct label or multiple distinct labels. Nevertheless, existing approaches usually separate feature embedding and distribution prediction. In this paper, we propose a novel supervised visual sentiment distribution prediction model, termed as low-rank regularized multi-view inverse-covariance estimation, in which feature embedding and distribution prediction are jointly performed. Specifically, our proposed model contains two main components: multi-view embedding and inverse-covariance estimation terms. The multi-view embedding term is restricted by low-rank constraints to seek the lowest-rank representation of samples. The inverse-covariance estimation term is restricted by structured sparsity regularization to learn a more reasonable distribution prediction model. We develop an alternative heuristic optimization algorithm to solve the objective function of the proposed model. Experiment results performed on three publicly available datasets demonstrate the effectiveness of our proposed scheme compared with state-of-the-art algorithms.