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Conference Paper
Recognition of Meaningful Human Actions for Video Annotation Using EEG Based User Responses
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- Authors
- Jinyoung Moon, Yongjin Kwon, Kyuchang Kang, Changseok Bae, Wan Chul Yoon
- Issue Date
- 2015-01
- Citation
- International Conference on MultiMedia Modeling (MMM) 2015 (LNCS 8936), v.8936, pp.447-457
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- Abstract
- To provide interesting videos, it is important to generate relevant tags and annotations that describe the whole video or its segment efficiently. Because generating annotations and tags is a time-consuming process, it is essential for analyzing videos without human intervention. Although there have been many studies of implicit human-centered tagging using bio-signals, most of them focus on affective tagging and tag relevance assessment. This paper proposes binary and unary classification models that recognize actions meaningful to users in videos, for example jumps in the figure skating program, using EEG features of band power (BP) values and asymmetry scores (AS). As a result, the binary and binary classification models achieved the best balanced accuracies of 52.86% and 50.06% respectively. The binary classification models showed high specificity on non-jump actions and the unary classification models showed high sensitivity on jump actions.
- KSP Keywords
- Band Power, Binary Classification, Classification models, EEG features, High Sensitivity, High specificity, Human action, Tag Relevance, bio-signal, human intervention, human-centered