지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.11 No.1
- 발행연도
- 2017.3
- 수록면
- 24 - 31 (8page)
이용수
초록· 키워드
오류제보하기
Face-based video retrieval has become an active and important branch of intelligent video analysis. Face profiling and matching is a fundamental step and is crucial to the effectiveness of video retrieval. Although many algorithms have been developed for processing static face images, their effectiveness in face-based video retrieval is still unknown, simply because videos have different resolutions, faces vary in scale, and different lighting conditions and angles are used. In this paper, we combined content-based and semantic-based image analysis techniques, and systematically evaluated four mainstream local features to represent face images in the video retrieval task: Harris operators, SIFT and SURF descriptors, and eigenfaces. Results of ten independent runs of 10-fold cross-validation on datasets consisting of TED (Technology Entertainment Design) talk videos showed the effectiveness of our approach, where the SIFT descriptors achieved an average F-score of 0.725 in video retrieval and thus were the most effective, while the SURF descriptors were computed in 0.3 seconds per image on average and were the most efficient in most cases.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. CONSTRUCTING FACE PROFILES USING LOCAL FEATURES
IV. EXPERIMENTAL SETUP
V. EXPERIMENTAL RESULTS AND DISCUSSION
VI. CONCLUSION
REFERENCES
참고문헌 (22)
참고문헌 신청
P. Geetha and V. Narayanan, “A survey of content-based video retrieval,” Journal of Computer Science, vol. 4, no. 6, pp. 474-486, 2008.
R. Belaroussi and M. Milgram, “A comparative study on face detection and tracking algorithms,” Expert Systems with Applications, vol. 39, no. 8, pp. 7158-7164, 2012.
Y. Chen, X. Li, A. Dick, and R. Hill, “Ranking consistency for image matching and object retrieval,” Pattern Recognition, vol. 47, no. 3, pp. 1349-1360, 2014.
F. Hopfgartner, “Personalised video retrieval: application of implicit feedback and semantic user profiles,” Ph.D. dissertation, University of Glasgow, UK, 2010.
S. Yu, L. Jiang, Z. Xu, Y. Yang, and A. G. Hauptmann, “Content-based video search over 1million videos with 1 core in 1 second,” in Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (ICMR), Shanghai, China, 2015, pp. 419-426.
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UCI(KEPA) : I410-ECN-0101-2017-569-002394088