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Web 2.0 dictionary

Published: 07 July 2008 Publication History

Abstract

How might we benefit from the billions of tagged multimedia files (e.g. image, video, audio) available on the Internet? This paper presents a new concept called Web 2.0 Dictionary, a dynamic dictionary that takes advantage of, and is in fact built from, the huge database of tags available on the Web.
The Web 2.0 Dictionary distinguishes itself from the traditional dictionary in six main ways: (1) it is fully automatic because it downloads tags from the Web and inserts this new information into the dictionary; (2) it is dynamic because each time a new shared image/video is uploaded, a "bag-of-tags" corresponding to the image/video will be downloaded, thus updating Web 2.0 Dictionary. The Web 2.0 Dictionary is literally updating every second, which is not true of the traditional dictionary; (3) it integrates all kinds of languages (e.g. English, Chinese), as long as the images/videos are tagged with words from such languages; (4) it is built by distilling a small amount of useful information from a massive and noisy tag database maintained by the entire Internet community, therefore the relatively small amount of noise present in the database will not affect it; (5) it truly reflects the most prevalent and relevant explanations in the world, unaffected by majoritarian views and political leanings. It is a real, free dictionary. Unlike Wikipedia" [5] which can be easily revised by even a single person, the Web 2.0 Dictionary is very stable because its contents are informed by a whole community of users that upload photo/videos; (6) it provides a correlation value between every two words ranging from 0 to 1. The correlation values stored in the dictionary have wide applications. We demonstrate the effectiveness of the Web 2.0 Dictionary for image/video understanding and retrieval, object categorization, tagging recommendation, etc, in this paper.

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  • (2017)A collaborative approach for semantic time-based video annotation using gamificationHuman-centric Computing and Information Sciences10.1186/s13673-017-0094-57:1(1-21)Online publication date: 1-Dec-2017
  • (2015)[Invited Paper] A Review of Web Image MiningITE Transactions on Media Technology and Applications10.3169/mta.3.1563:3(156-169)Online publication date: 2015
  • (2015)VisualTextualRank: An Extension of VisualRank to Large-Scale Video Shot Extraction Exploiting Tag Co-occurrenceIEICE Transactions on Information and Systems10.1587/transinf.2014EDP7106E98.D:1(166-172)Online publication date: 2015
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cover image ACM Conferences
CIVR '08: Proceedings of the 2008 international conference on Content-based image and video retrieval
July 2008
674 pages
ISBN:9781605580708
DOI:10.1145/1386352
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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Publication History

Published: 07 July 2008

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Author Tags

  1. bag-of-tags
  2. photo/video sharing
  3. rank
  4. retrieval
  5. tag

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Cited By

View all
  • (2017)A collaborative approach for semantic time-based video annotation using gamificationHuman-centric Computing and Information Sciences10.1186/s13673-017-0094-57:1(1-21)Online publication date: 1-Dec-2017
  • (2015)[Invited Paper] A Review of Web Image MiningITE Transactions on Media Technology and Applications10.3169/mta.3.1563:3(156-169)Online publication date: 2015
  • (2015)VisualTextualRank: An Extension of VisualRank to Large-Scale Video Shot Extraction Exploiting Tag Co-occurrenceIEICE Transactions on Information and Systems10.1587/transinf.2014EDP7106E98.D:1(166-172)Online publication date: 2015
  • (2014)Automatic extraction of relevant video shots of specific actions exploiting Web dataComputer Vision and Image Understanding10.1016/j.cviu.2013.03.009118(2-15)Online publication date: 1-Jan-2014
  • (2013)Large-scale web video shot ranking based on visual features and tag co-occurrenceProceedings of the 21st ACM international conference on Multimedia10.1145/2502081.2502139(525-528)Online publication date: 21-Oct-2013
  • (2012)Constructing visual tag dictionary by mining community-contributed media corpusNeurocomputing10.1016/j.neucom.2011.03.05995(3-10)Online publication date: 1-Oct-2012
  • (2011)Learning Visual Contexts for Image Annotation From Flickr GroupsIEEE Transactions on Multimedia10.1109/TMM.2010.210105113:2(330-341)Online publication date: 1-Apr-2011
  • (2011)Automatic construction of an action video shot database using web videosProceedings of the 2011 International Conference on Computer Vision10.1109/ICCV.2011.6126284(527-534)Online publication date: 6-Nov-2011
  • (2010)Tag dictionary and its applicationsProceedings of the international conference on Multimedia information retrieval10.1145/1743384.1743451(397-400)Online publication date: 29-Mar-2010
  • (2009)Visual tag dictionaryProceedings of the 1st workshop on Web-scale multimedia corpus10.1145/1631135.1631137(1-8)Online publication date: 23-Oct-2009
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