skip to main content
10.1145/3338533.3366594acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

Salient Time Slice Pruning and Boosting for Person-Scene Instance Search in TV Series

Published:10 January 2020Publication History

ABSTRACT

It is common that TV audiences want to quickly browse scenes with certain actors in TV series. Since 2016, the TREC Video Retrieval Evaluation (TRECVID) Instance Search (INS) task has started to focus on identifying a target person in a target scene simultaneously. In this paper, we name this kind of task as P-S INS (Person-Scene Instance Search). To find out P-S instances, most approaches search person and scene separately, and then directly combine the results together by addition or multiplication. However, we find that person and scene INS modules are not always effective at the same time, or they may suppress each other in some situations. Aggregating the results shot after shot is not a good choice. Luckily, for the TV series, video shots are arranged in chronological order. We extend our focus from time point (single video shot) to time slice (multiple consecutive video shots) in the time-line. Through detecting salient time slices, we prune the data. Through evaluating the importance of salient time slices, we boost the aggregation results. Extensive experiments on the large-scale TRECVID INS dataset demonstrate the effectiveness of the proposed method.

References

  1. George Awad, Wessel Kraaij, Paul Over, and Shin'ichi Satoh. 2017. Instance search retrospective with focus on TRECVID. International journal of multimedia information retrieval (2017).Google ScholarGoogle ScholarCross RefCross Ref
  2. Mika Fischer, Hazım Kemal Ekenel, and Rainer Stiefelhagen. 2011. Person re-identification in tv series using robust face recognition and user feedback. Multimedia Tools and Applications (2011).Google ScholarGoogle Scholar
  3. Haiyun Guo, Jinqiao Wang, Yue Gao, Jianqiang Li, and Hanqing Lu. 2016. Multi-view 3d object retrieval with deep embedding network. TIP (2016).Google ScholarGoogle Scholar
  4. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR.Google ScholarGoogle Scholar
  5. Luis Herranz, Shuqiang Jiang, and Xiangyang Li. 2016. Scene recognition with CNNs: objects, scales and dataset bias. In CVPR.Google ScholarGoogle Scholar
  6. Jiamei Lan, Jun Chen, Zheng Wang, Chao Liang, and Shin'ichi Satoh. 2017. PS Instance Retrieval via Early Elimination and Late Expansion. In ACM MM Workshop.Google ScholarGoogle Scholar
  7. Duy-Dinh Le, Sang Phan, and Shin'ichi Satoh. 2016. NII-HITACHI-UIT at TRECVID 2016. In TRECVID Workshop.Google ScholarGoogle Scholar
  8. Duy-Dinh Le, Sebastien Poullot, Xiaomeng Wu, Bertrand Nouvel, and Shin'ichi Satoh. 2010. National Institute of Informatics, Japan at TRECVID 2010.. In TRECVID Workshop.Google ScholarGoogle Scholar
  9. Jingjing Meng, Junsong Yuan, Yap-Peng Tan, and Gang Wang. 2015. Fast object instance search in videos from one example. In ICIP.Google ScholarGoogle Scholar
  10. Vinh-Tiep Nguyen, Dinh-Luan Nguyen, Minh-Triet Tran, Duy-Dinh Le, Duc Anh Duong, and Shin'ichi Satoh. 2015. Query-adaptive late fusion with neural network for instance search. In MMSP.Google ScholarGoogle Scholar
  11. Yuxin Peng, Xin Huang, and Jinwei Qi. 2016. Pku-icst at trecvid 2016: Instance search task. In TRECVID Workshop.Google ScholarGoogle Scholar
  12. Gerard Salton and Donna Harman. 2003. Information retrieval. John Wiley and Sons Ltd.Google ScholarGoogle Scholar
  13. Alan F Smeaton, Paul Over, and Wessel Kraaij. 2006. Evaluation campaigns and TRECVid. In ACM international workshop on Multimedia information retrieval.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Zheng Wang, Yang Yang, Shuosen Guan, and Chenxia Han. 2016. Whu-nercms at trecvid2016: Instance search task. In TRECVID Workshop.Google ScholarGoogle Scholar
  15. Wei Zhang, Hongzhi Li, Chong-Wah Ngo, and Shih-Fu Chang. 2014. Scalable visual instance mining with threads of features. In ACM MM.Google ScholarGoogle Scholar
  16. W Zhang, CC Tan, SA Zhu, T Yao, L Pang, and CW Ngo. 2012. Vireo@ trecvid 2012: Searching with topology, recounting will small concepts, learning with free examples. In TRECVID Workshop.Google ScholarGoogle Scholar
  17. Zhenxing Zhang, Rami Albatal, Cathal Gurrin, and Alan F Smeaton. 2013. Trecvid 2013 experiments at dublin city university. In TRECVID Workshop.Google ScholarGoogle Scholar
  18. Zhicheng Zhao, Menglai Wang, and Rui Xiang. 2016. Bupt-mcprl at trecvid 2016. In TRECVID Workshop.Google ScholarGoogle Scholar
  19. Liang Zheng, Yi Yang, and Qi Tian. 2017. SIFT meets CNN: A decade survey of instance retrieval. TPAMI (2017).Google ScholarGoogle Scholar
  20. Yousong Zhu, Jinqiao Wang, Chaoyang Zhao, Haiyun Guo, and Hanqing Lu. 2016. Scale-adaptive deconvolutional regression network for pedestrian detection. In ACCV.Google ScholarGoogle Scholar

Index Terms

  1. Salient Time Slice Pruning and Boosting for Person-Scene Instance Search in TV Series

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            MMAsia '19: Proceedings of the 1st ACM International Conference on Multimedia in Asia
            December 2019
            403 pages
            ISBN:9781450368414
            DOI:10.1145/3338533

            Copyright © 2019 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 10 January 2020

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed limited

            Acceptance Rates

            MMAsia '19 Paper Acceptance Rate59of204submissions,29%Overall Acceptance Rate59of204submissions,29%

            Upcoming Conference

            MM '24
            MM '24: The 32nd ACM International Conference on Multimedia
            October 28 - November 1, 2024
            Melbourne , VIC , Australia

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader