상세정보
학술대회
Recognition of Meaningful Human Actions for Video Annotation Using EEG Based User Responses
- 발행일
- 201501
- 출처
- International Conference on MultiMedia Modeling (MMM) 2015 (LNCS 8936), v.8936, pp.447-457
- 협약과제
- 14MS4500, (1세부) 실시간 대규모 영상 데이터 이해·예측을 위한 고성능 비주얼 디스커버리 플랫폼 개발, 박경
- 초록
- 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 제안 키워드
- Band Power, Binary Classification, Classification models, EEG features, High Sensitivity, High specificity, Human Action, Tag Relevance, Video Annotation, bio-signal, human intervention