short-paper

News Program Detection in TV Broadcast Videos

Authors:

Raghvendra Kannao,

Durgaprasad Dandi,

Prithwijit GuhaAuthors Info & Claims

MM '16: Proceedings of the 24th ACM international conference on Multimedia

Pages 546 - 550

https://doi.org/10.1145/2964284.2967281

Published: 01 October 2016 Publication History

Abstract

Television news channels broadcast different kinds of content like debates, interviews, commercials along with news presentations. Real-time detection of these news programs or their retrieval from large volumes of stored broadcast videos is a challenging problem and is a necessary first step for broadcast analytics. News program detection is even harder in Indian context where closed caption text or program markers are not provided by TV news channels (not mandated by law). We propose a two-stage approach to classify news video segments. First, broadcast video shots are classified with multiple labels based on a set of audio-visual features. Second, sequences of these shot features are modeled to detect news programs. Another contribution of this work is the construction of a dataset of 120 hours of shot categories and news programs from Indian English news channels. We have experimented with SVM, HMM and CRF based classifiers and achieved a F1 score of 99% in detecting news programs while experimenting on our dataset.

References

[1]

P. K. Atrey, M. A. Hossain, S. A. El, and M. S. Kankanhalli. Multimodal fusion for multimedia analysis: a survey. Multimedia systems, 16(6):345--379, 2010.

Digital Library

[2]

S. P. Chatzis and Y. Demiris. The infinite-order conditional random field model for sequential data modeling. Pattern Analysis and Machine Intelligence, 35(6):1523--1534, 2013.

Digital Library

[3]

Y. Chen, Z. Li, M. Li, and W.-Y. Ma. Automatic classification of photographs and graphics. In ICME, pages 973--976. IEEE, 2006.

[4]

T. G. Dietterich. Machine learning for sequential data: A review. In Structural, syntactic, and statistical pattern recognition, pages 15--30. Springer, 2002.

Digital Library

[5]

N. Dimitrova, L. Agnihotri, and G. Wei. Video classification based on hmm using text and faces. In European Signal Processing Conference, 2000.

[6]

L. Elin and A. Lapides. Designing and Producing the Television Commercial. Pearson/A and B, 2004.

[7]

R. Kannao and P. Guha. Tv commercial detection using success based locally weighted kernel combination. In MultiMedia Modeling, pages 793--805. Springer, 2016.

Digital Library

[8]

W. Kim, J. Park, and C. Kim. A novel method for efficient indoor--outdoor image classification. Journal of Signal Processing Systems, 61(3):251--258, 2010.

Digital Library

[9]

J. Lafferty, A. McCallum, and F. C. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. 2001.

Digital Library

[10]

H. Li, B. Jou, J. G. Ellis, D. Morozoff, and S.-F. Chang. News rover: Exploring topical structures and serendipity in heterogeneous multimedia news. In ACM MM, pages 449--450. ACM, 2013.

Digital Library

[11]

L. R. Rabiner and B.-H. Juang. An introduction to hidden markov models. ASSP Magazine, IEEE, 3(1):4--16, 1986.

[12]

L. Rokach. Ensemble-based classifiers. Artif. Intell. Rev., 33(1--2):1--39, 2010.

Digital Library

[13]

M. Szummer and R. W. Picard. Indoor-outdoor image classification. In Workshop on Content-Based Access of Image and Video Database, pages 42--51. IEEE.

Digital Library

[14]

S. Vishwanathan, A. J. Smola, et al. Fast kernels for string and tree matching. Kernel methods in computational biology, pages 113--130, 2004.

[15]

Wikipedia. Dayparting -- Wikipedia, the free encyclopedia. https://en.wikipedia.org/wiki/Dayparting, 2016. {Online; accessed 22-July-2016}.

[16]

S. Xu, B. Feng, Z. Chen, and B. Xu. A general framework of video segmentation to logical unit based on conditional random fields. In ICMR, pages 247--254, 2013.

Digital Library

[17]

A. Yanagawa, W. Hsu, and S.-F. Chang. Anchor shot detection in trecvid-2005 broadcast news videos. Technical report, Columbia ADVENT Technical Report, 2005.

[18]

L. Zhang, Z. Zhu, and Y. Zhao. Robust commercial detection system. In ICME, pages 587--590, 2007.

[19]

H. Zhou, T. Hermans, A. V. Karandikar, and J. M. Rehg. Movie genre classification via scene categorization. In ACM MM, pages 747--750. ACM, 2010.

Digital Library

[20]

T. Zlitni, B. Bouaziz, and W. Mahdi. Automatic topics segmentation for tv news video using prior knowledge. Multimedia Tools and Applications, pages 1--28, 2015.

Digital Library

Cited By

Kannao RGuha PChaudhuri B(2022)Only overlay text: novel features for TV news broadcast video segmentationMultimedia Tools and Applications10.1007/s11042-022-12917-w81:21(30493-30517)Online publication date: 6-Apr-2022
https://doi.org/10.1007/s11042-022-12917-w
Kannao RGuha P(2019)A system for semantic segmentation of TV news broadcast videosMultimedia Tools and Applications10.1007/s11042-019-08445-979:9-10(6191-6225)Online publication date: 13-Dec-2019
https://doi.org/10.1007/s11042-019-08445-9
Yu JLei ASong ZWang TCai HFeng N(2018)Comprehensive Dataset of Broadcast Soccer Videos2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2018.00090(418-423)Online publication date: Apr-2018
https://doi.org/10.1109/MIPR.2018.00090
Show More Cited By

Recommendations

CNN-based Commercial Detection in TV Broadcasting
ICNCC '17: Proceedings of the 2017 VI International Conference on Network, Communication and Computing

TV is an important advertising media. Information of a piece of TV commercial, such as broadcasting time, the duration, the casting and etc., may reflect the business value of the host company of this commercial. An automatic commercial detection system ...
Semantic event detection in baseball videos based on a multi-output hidden Markov model
SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

In this paper, we proposed an event detection method in baseball videos based on a multi-output HMM (hidden Markov model), using high-level audio/video features. For the video part, we use eight kinds of semantic scenes detected from baseball videos in ...
Combining experts for anchorperson shot detection in news videos

Automatic classification of shots extracted by news videos plays an important role in the context of news video segmentation, which is an essential step towards effective indexing of broadcasters’ digital databases. In spite of the efforts reported by ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '16: Proceedings of the 24th ACM international conference on Multimedia

October 2016

1542 pages

ISBN:9781450336031

DOI:10.1145/2964284

General Chairs:
Alan Hanjalic
Delft University of Technology
,
Cees Snoek
Qualcomm Research Netherlands / University of Amsterdam
,
Marcel Worring
University of Amsterdam
,
Moderator:
Dick Bulterman
CWI / VU University Amsterdam
,
Program Chairs:
Benoit Huet
EURECOM
,
Aisling Kelliher
Virginia Tech
,
Yiannis Kompatsiaris
CERTH-ITI
,
Jin Li
Microsoft

Copyright © 2016 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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

MM '16

Sponsor:

SIGMM

MM '16: ACM Multimedia Conference

October 15 - 19, 2016

Amsterdam, The Netherlands

Acceptance Rates

MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
159
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)1

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kannao RGuha PChaudhuri B(2022)Only overlay text: novel features for TV news broadcast video segmentationMultimedia Tools and Applications10.1007/s11042-022-12917-w81:21(30493-30517)Online publication date: 6-Apr-2022
https://doi.org/10.1007/s11042-022-12917-w
Kannao RGuha P(2019)A system for semantic segmentation of TV news broadcast videosMultimedia Tools and Applications10.1007/s11042-019-08445-979:9-10(6191-6225)Online publication date: 13-Dec-2019
https://doi.org/10.1007/s11042-019-08445-9
Yu JLei ASong ZWang TCai HFeng N(2018)Comprehensive Dataset of Broadcast Soccer Videos2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2018.00090(418-423)Online publication date: Apr-2018
https://doi.org/10.1109/MIPR.2018.00090
Kannao RGuha PBatra DBrown MNatarajan V(2016)Generic TV advertisement detection using progressively balanced perceptron treesProceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3009977.3009995(1-8)Online publication date: 18-Dec-2016
https://dl.acm.org/doi/10.1145/3009977.3009995

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents