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Leveraging collective wisdom for web video retrieval through heterogeneous community discovery

Published: 28 November 2011 Publication History

Abstract

With the exponential growth of social media, web video retrieval based on contextual information associated with videos has attracted wide attention recently. However, state-of-the-art methods mainly focus on limited kinds of context cues and lack of unified exploration towards multiple heterogeneous contexts. In this paper, we propose a novel web video ranking framework called CommunityRank by leveraging the collective wisdom from a community perspective. Firstly, it formulizes various social relations among users, videos and tags in a heterogeneous context network and further detects its latent community structure. Then the algorithm maps videos into the community space and performs a community-oriented re-ranking through a bipartite graph model. By aggregating the multiple relations, CommunityRank can make the most of textual, visual and social contexts and leads to better search results. The encouraging performances of the proposed method on YouTube video collection demonstrate that the discovered communities reveal topics of interest emerging in collective behaviors and can facilitate web video retrieval.

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  1. Leveraging collective wisdom for web video retrieval through heterogeneous community discovery

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    cover image ACM Conferences
    MM '11: Proceedings of the 19th ACM international conference on Multimedia
    November 2011
    944 pages
    ISBN:9781450306164
    DOI:10.1145/2072298
    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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    New York, NY, United States

    Publication History

    Published: 28 November 2011

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    Author Tags

    1. heterogeneous community discovery
    2. social media
    3. video retrieval

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    MM '11
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    MM '11: ACM Multimedia Conference
    November 28 - December 1, 2011
    Arizona, Scottsdale, USA

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    • (2016)Web video topics discovery and structuralization with social networkNeurocomputing10.1016/j.neucom.2014.10.103172(53-63)Online publication date: Jan-2016
    • (2016)On application-unbiased benchmarking of web videos from a social network perspectiveMultimedia Tools and Applications10.1007/s11042-014-2245-275:3(1543-1556)Online publication date: 1-Feb-2016
    • (2014)IntroductionUser-centric Social Multimedia Computing10.1007/978-3-662-44671-3_1(1-9)Online publication date: 18-Oct-2014
    • (2013)Social influence analysis and application on multimedia sharing websitesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/25024369:1s(1-24)Online publication date: 17-Oct-2013
    • (2012)Collective search and recommendation in social mediaProceedings of the 20th ACM international conference on Multimedia10.1145/2393347.2396509(1421-1424)Online publication date: 29-Oct-2012
    • (2012)Right buddy makes the differenceProceedings of the 20th ACM international conference on Multimedia10.1145/2393347.2393358(19-28)Online publication date: 29-Oct-2012

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