Multiple Emotion Tagging for Multimedia Data by Exploiting High-Order Dependencies Among Emotions

In this paper, a novel approach of multiple emotional multimedia tagging is proposed, which explicitly models the higher-order relations among emotions. First, multimedia features are extracted from the multimedia data. Second, a traditional multi-label classifier is used to obtain the measurements of the multi-emotion labels. Then, we propose a three-layer restricted Boltzmann machine (TRBM) model to capture the higher-order relations among emotion labels, as well as the relations between labels and measurements . Finally , the TRBM model is used to infer the samples´ multi- emotion labels by combining the emotion measurements with the dependencies among multi- emotions . Experimental results on four databases demonstrate that our method is more effective than both feature -driven methods and current model-based methods, which capture the pairwise relations among labels by the Bayesian network (BN). Furthermore , the comparison of BN models and the proposed TRBM model verifies that the patterns captured by the latent units of TRBM contain not only all the dependencies captured by the BN but also many other dependencies that the BN cannot capture.