skip to main content
10.1145/2487575.2487715acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
demonstration

An online system with end-user services: mining novelty concepts from tv broadcast subtitles

Published: 11 August 2013 Publication History

Abstract

Better tools for content-based access of video are needed to improve access to time-continuous video data. Particularly information about linear TV broadcast programs has been available in a form limited to program guides that provide short manually described overviews of the program content. Recent development in digitalization of TV broadcasting and emergence of web-based services for catch-up and on-demand viewing bring out new possibilities to access data. In this paper we introduce our data mining system and accompanying services for summarizing Finnish DVB broadcast streams from seven national channels. We describe how data mining of novelty concepts can be extracted from DVB subtitles to augment web-based "Catch-Up TV Guide" and "Novelty Cloud" TV services. Furthermore, our system allows accessing media fragments as Picture Quotes via generated word lists and provides content-based recommendations to find new programs that have content similar to the user selected programs. Our index consists of over 180 000 programs that are used to recommend relevant programs. The service has been under development and available online since 2010. It has registered over 5000 user sessions.

References

[1]
Mythtv, open source dvr. http://www.mythtv.org/.
[2]
Voikko - free linguistic software for finnish. http://voikko.sourceforge.net/.
[3]
Kuukkelitv.fi. KuukkeliTV - Mediaseinä. http://www.kuukkelitv.fi/mediaseina.
[4]
Kuukkelitv.fi. KuukkeliTV - Uutispilvi. http://www.kuukkelitv.fi/uutispilvi.
[5]
M. Markou and S. Singh. Novelty detection: a review--part 1: statistical approaches. Signal Processing, 83(12):2481 -- 2497, 2003.
[6]
Y. Matsuo and M. Ishizuka. Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools, 13(01):157--169, 2004.
[7]
P. Rayson and R. Garside. Comparing corpora using frequency profiling. In Proceedings of the Workshop on Comparing Corpora, CompareCorpora '00, pages 1--6, Stroudsburg, PA, USA, 2000. Association for Computational Linguistics.
[8]
Snowball. Snowball. http://snowball.tartarus.org/.
[9]
I. Soboroff and D. Harman. Novelty detection: the trec experience. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT '05, pages 105--112, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics.
[10]
Sukija. Sukija. http://sourceforge.net/projects/sukija/.
[11]
K. Zhang, H. Xu, J. Tang, and J. Li. Keyword extraction using support vector machine. In J. Yu, M. Kitsuregawa, and H. Leong, editors, Advances in Web-Age Information Management, volume 4016 of Lecture Notes in Computer Science, pages 85--96. Springer Berlin Heidelberg, 2006.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
August 2013
1534 pages
ISBN:9781450321747
DOI:10.1145/2487575
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 the author(s) 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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2013

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. broadcast data mining
  2. novelty concept detection
  3. online tv
  4. video analysis

Qualifiers

  • Demonstration

Conference

KDD' 13
Sponsor:

Acceptance Rates

KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 227
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media