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- Title
Multiple emotional tagging of multimedia data by exploiting dependencies among emotions.
- Authors
Wang, Shangfei; Wang, Zhaoyu; Ji, Qiang
- Abstract
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 data's 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.
- Subjects
MULTIMEDIA systems; EMOTIONS in motion pictures; BAYESIAN analysis; TAGS (Metadata); SUPPORT vector machines
- Publication
Multimedia Tools & Applications, 2015, Vol 74, Issue 6, p1863
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-013-1722-3