Multiple emotional tagging of multimedia data by exploiting dependencies among emotions
Digital multimedia may elicit a mixture of human emotions. Most current emotional tagging research typically tags the multimedia data with a single emotion, ignoring the phenomenon of multi-emotion coexistence. To address this problem, we propose a novel multi-emotion tagging approach by explicitly modeling the dependencies among emotions. First, several audio or visual features are extracted from the multimedia data. Second, four traditional multi-label learning methods: Binary Relevance, Random k label sets, Binary Relevance k Nearest Neighbours and Multi-Label k Nearest Neighbours, are used as the classifiers to obtain the measurements of emotional tags. Then, a Bayesian network is automatically constructed to capture the relationships among emotional tags. Finally, the Bayesian network is used to infer the datas multi-emotion tags by combining the measurements obtained from those traditional methods with the dependencies among emotions. Experiments on two multi-label media data sets demonstrate the superiority of our approach to the existing methods.
multiple emotional tagging multimedia bayesian network multi-label classification