Skip to main content
SpringerLink
Log in

Video News Retrieval Incorporating Relevant Terms Based on Distribution of Document Frequency

  • Conference paper
Advances in Multimedia Information Processing - PCM 2008 (PCM 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5353))

Included in the following conference series:

Abstract

This paper presents an approach to video news retrieval within an event by integrating visual and textual features. A set of histogram bins of key frames in a shot is adopted as the visual feature, while the term frequency is used as the textual feature. A term scoring method is proposed to enhance the weights of relevant terms in an event by considering the windowed document frequency distribution. The weight for a given term is determined by mean of the difference between usual and unusual term groups which are quantized by the boxplot method. The first experiment evaluate the performance of the proposed method by giving generated document frequency distributions, while the second experiment gives the desired retrieval results for relevant terms in the real data. It concludes the proposed method can increase the performance of retrieving video news stories within an event using relevant terms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. http://ckipsvr.iis.sinica.edu.tw/

  2. Becker, J., Kuropka, D.: Topic-based Vector Space Model. In: BIS 2003 (2003)

    Google Scholar 

  3. Li, X., Wang, D., Li, J., Zhang, B.: Video Search in Concept Subspace: A Text-Like Paradigm. In: CIVR 2007 (2007)

    Google Scholar 

  4. Liu, Y., Mei, T., Qi, G., Wu, X., Hua, X.-S.: Query-Independent Learning for Video Search. In: ICME 2008 (2008)

    Google Scholar 

  5. Peng, Y., Ngo, C.W.: Clip-Based Similarity Measure for Query-Dependent Clip Retrieval and Video Summarization. IEEE Trans. Circuits and Systems for Video Technology 16, 612–627 (2006)

    Article  Google Scholar 

  6. Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18, 613–620 (1975)

    Article  MATH  Google Scholar 

  7. Volkmer, T., Natsev, A.P.: Exploring Automatic Query Refinement for Text-Based Video Retrieval. In: ICME 2006 (2006)

    Google Scholar 

  8. Wu, H.C., Luk, R.W.P., Wong, K.F., Kwok, K.L.: Interpreting TF-IDF term weights as making relevance decisions. ACM Transactions on Information Systems 26, 1–37 (2008)

    Article  Google Scholar 

  9. Walpole, R.E., Myers, R.H., Myers, S.L., Ye, K.: Probability & Statistics for Engineers & Scientists. Pearson, London

    Google Scholar 

  10. Wang, H.M., Chen, B., Kuo, J.W., Cheng, S.S.: MATBN: A Mandarin Chinese Broadcast News Corpus. Computational Linguistics and Chinese Language Processing 10, 219–236 (2005)

    Google Scholar 

  11. Yeo, B.L., Liu, B.: Rapid Scene Analysis on Compressed Video. IEEE Trans. Circuits and Systems for Video Technology 5, 533–544 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

    Jun-Bin Yeh & Chung-Hsien Wu

Authors
  1. Jun-Bin Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chung-Hsien Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Engineering Science, National Cheng Kung University, No.1, University Road, 701, Tainan City, Taiwan

    Yueh-Min Ray Huang

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95, Zhongguancun East Road, 100190, Beijing, China

    Changsheng Xu

  3. Institute of Biomedical Engineering, National Cheng Kung University, No. 1, University Road, 701, Tainan City, Taiwan

    Kuo-Sheng Cheng

  4. Department of Electrical Engineering, National Cheng Kung University, No. 1, University Road, 701, Tainan City, Taiwan

    Jar-Ferr Kevin Yang

  5. Department of Electrical and Computer Engineering, Concordia University, S-EV005.139, 1515 St. Catherine West, Montreal, H4G 2W1, Quebec, Canada

    M. N. S. Swamy

  6. Microsoft Research Asia, 5/F, Beijing Sigma Center, No. 49, Zhichun Road, Hai Dian District, 100080, Beijing, China

    Shipeng Li

  7. Department of Information Management, National Kaohsiung University of Applied Sciences, No. 415, Jiangong Road, Sanmin District, 80778, Kaohsiung, Taiwan

    Jen-Wen Ding

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yeh, JB., Wu, CH. (2008). Video News Retrieval Incorporating Relevant Terms Based on Distribution of Document Frequency. In: Huang, YM.R., et al. Advances in Multimedia Information Processing - PCM 2008. PCM 2008. Lecture Notes in Computer Science, vol 5353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89796-5_60

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89796-5_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89795-8

  • Online ISBN: 978-3-540-89796-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation