Article

Cast indexing for videos by NCuts and page ranking

Authors:

Wei Hu,

HaiZhou AiAuthors Info & Claims

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

Pages 441 - 447

https://doi.org/10.1145/1282280.1282345

Published: 09 July 2007 Publication History

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Abstract

Cast indexing is an important video mining technique which provides audience the capability to efficiently retrieve interested scenes, events, and stories from a long video. This paper proposes a novel cast indexing approach based on Normalized Graph Cuts (NCuts) and Page Ranking. The system first adopts face tracker to group face images in each shot into face sets, and then extract local SIFT feature as the feature representation. There are two key problems for cast indexing. One is to find an optimal partition to cluster face sets into main cast. The other is how to exploit the latent relationships among characters to provide a more accurate cast ranking. For the first problem, we model each face set as a graph node, and adopt Normalized Graph Cuts (NCuts) to realize an optimal graph partition. A novel local neighborhood distance is proposed to measure the distance between face sets for NCuts, which is robust to outliers. For the second problem, we build a relation graph for characters by their co-occurrence information, and then adopt the PageRank algorithm to estimate the Important Factor (IF) of each character. The PageRank IF is fused with the content based retrieval score for final ranking. Extensive experiments are carried out on movies, TV series and home videos. Promising results demonstrate the effectiveness of proposed methods.

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Cited By

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Wolf KAbdelrahman YLandwehr MWard GSchmidt AHäkkila JOjala T(2016)How to browse through my large video dataProceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia10.1145/3012709.3012713(169-173)Online publication date: 12-Dec-2016
https://dl.acm.org/doi/10.1145/3012709.3012713
Ramezani MYaghmaee F(2016)A review on human action analysis in videos for retrieval applicationsArtificial Intelligence Review10.1007/s10462-016-9473-y46:4(485-514)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s10462-016-9473-y
Lobo MSingh MKannao RGuha PRamakrishnan AMalik JEfros AJawahar CVarma M(2014)A Novel Method for Face Track Linking in VideosProceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing10.1145/2683483.2683551(1-5)Online publication date: 14-Dec-2014
https://dl.acm.org/doi/10.1145/2683483.2683551
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Index Terms

Cast indexing for videos by NCuts and page ranking
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

July 2007

655 pages

ISBN:9781595937339

DOI:10.1145/1282280

General Chairs:
Nicu Sebe
Univ. of Amsterdam, The Netherlands
,
Marcel Worring
Univ. of Amsterdam, The Netherlands

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]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 09 July 2007

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CIVR07: International Conference on Image and Video Retrieval 2007

July 9 - 11, 2007

Amsterdam, The Netherlands

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Wolf KAbdelrahman YLandwehr MWard GSchmidt AHäkkila JOjala T(2016)How to browse through my large video dataProceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia10.1145/3012709.3012713(169-173)Online publication date: 12-Dec-2016
https://dl.acm.org/doi/10.1145/3012709.3012713
Ramezani MYaghmaee F(2016)A review on human action analysis in videos for retrieval applicationsArtificial Intelligence Review10.1007/s10462-016-9473-y46:4(485-514)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s10462-016-9473-y
Lobo MSingh MKannao RGuha PRamakrishnan AMalik JEfros AJawahar CVarma M(2014)A Novel Method for Face Track Linking in VideosProceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing10.1145/2683483.2683551(1-5)Online publication date: 14-Dec-2014
https://dl.acm.org/doi/10.1145/2683483.2683551
Wang TGao YWang PHu WLi JDu YZhang Y(2011)Video Summarization by Redundancy Removing and Content RankingComputer Vision for Multimedia Applications10.4018/978-1-60960-024-2.ch006(91-101)Online publication date: 2011
https://doi.org/10.4018/978-1-60960-024-2.ch006
Yuan KYao HJi RSun X(2010)Mining actor correlations with hierarchical concurrence parsing2010 IEEE International Conference on Acoustics, Speech and Signal Processing10.1109/ICASSP.2010.5494953(798-801)Online publication date: Mar-2010
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Shan C(2010)Face Recognition and Retrieval in VideoVideo Search and Mining10.1007/978-3-642-12900-1_9(235-260)Online publication date: 2010
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Yuan KJi RYao HSun XXu PLiu XLi SLu HXu DBimbo ATian QXu C(2009)VisualCor systemProceedings of the First International Conference on Internet Multimedia Computing and Service10.1145/1734605.1734655(213-218)Online publication date: 23-Nov-2009
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Zhang YXu CLu HHuang Y(2009)Character identification in feature-length films using global face-name matchingIEEE Transactions on Multimedia10.1109/TMM.2009.203062911:7(1276-1288)Online publication date: 1-Nov-2009
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Li ELi WTong XLi JChen YWang TWang PHu WDu YZhang YChen Y(2008)Accelerating Video-Mining Applications Using Many Small, General-Purpose CoresIEEE Micro10.1109/MM.2008.6428:5(8-21)Online publication date: 1-Sep-2008
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