Finding Important People in a Video Using Deep Neural Networks with Conditional Random Fields-Reference-Cited by-同舟云学术

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Finding Important People in a Video Using Deep Neural Networks with Conditional Random Fields

Published:2018-10-01 Issue:10 Volume:E101.D Page:2509-2517
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

OTANI Mayu¹,NISHIDA Atsushi²,NAKASHIMA Yuta³,SATO Tomokazu⁴,YOKOYA Naokazu¹

Affiliation:

1. Nara Institute of Science and Technology

2. Dai Nippon Printing Co., Ltd.

3. Osaka University

4. Shiga University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E101.D/10/E101.D_2018EDP7029/_pdf

Reference29 articles.

1. [1] F. Liu and M. Gleicher, “Video retargeting: Automating pan and scan,” ACM International Conference on Multimedia (MM), pp.241-250, 2006. 10.1145/1180639.1180702

2. [2] L. Itti, “Automatic foveation for video compression using a neurobiological model of visual attention,” IEEE Trans. Image Process., vol.13, no.10, pp.1304-1318, 2004. 10.1109/tip.2004.834657

3. [3] L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol.20, no.11, pp.1254-1259, 1998. 10.1109/34.730558

4. [4] Y.-F. Ma, X.-S. Hua, L. Lu, and H.-J. Zhang, “A generic framework of user attention model and its application in video summarization,” IEEE Trans. Multimedia, vol.7, no.5, pp.907-919, 2005. 10.1109/tmm.2005.854410

5. [5] Y. Nakashima, N. Babaguchi, and J. Fan, “Intended human object detection for automatically protecting privacy in mobile video surveillance,” Multimedia Systems, vol.18, no.2, pp.157-173, 2012. 10.1007/s00530-011-0244-y

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