Skip to main content
SpringerLink
Log in

Visual Content Analysis

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems

Synonyms

Image indexing; Video indexing

Definition

Visual content analysis is the process of deriving meaningful descriptors for image and video data. These descriptors are the basis for searching large image and video collections. In practice, before the process starts, one applies image processing techniques which take the visual data, apply an operator, and return other visual data with less noise or specific characteristics of the visual data emphasized. The analysis considered in this contribution starts from here, ultimately aiming at semantic descriptors.

Historical Background

Analyzing the content of visual data using computers has a long history, dating back to the 1960s. Some initial successes prompted researchers in the 1970s to predict that the problem of understanding visual material would soon be solved completely. However, the research in the 80s showed that these predictions were far too optimistic. Even now, understanding visual data is still a major challenge.

In the...

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 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover 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

Recommended Reading

  1. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. 2001. Software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  2. Everingham M, van Gool L, Williams C, Winn J, Zisserman A. The PASCAL visual object classes homepage. Available at: http://www.pascal-network.org/challenges/VOC/.

  3. Gemert J, Geusebroek J, Veenman C, Snoek C, Smeulders A. Robust scene categorization by learning image statistics in context. In: Proceedings of the International Workshop on Semantic Learning Applications in Multimedia; 2006.

    Google Scholar 

  4. Geusebroek J, Boomgaard R, Smeulders A, Geerts H. Color invariance. IEEE Trans Pattern Anal Mach Intell. 2001;23(12):1338–50.

    Article  Google Scholar 

  5. Jain A, Duin R, Mao J. Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell. 2000;22(1):4–37.

    Article  Google Scholar 

  6. Jiang YG, Ngo CW, Yang J. VIREO-374: LSCOM semantic concept detectors using local keypoint features. Available at: http://www.cs.cityu.edu.hk/~yjiang/vireo374/.

  7. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.

    Article  MathSciNet  Google Scholar 

  8. Sikora T. The MPEG-7 visual standard for content description-an overview. IEEE Trans Circ Syst Video Tech. 2001;11(6):696–702.

    Article  Google Scholar 

  9. Smeaton A. Large scale evaluations of multimedia information retrieval: the TRECVid experience. In: Proceedings of the 4th International Conference on Image and Video Retrieval; 2005. p. 19–27.

    Google Scholar 

  10. Smeulders A, Worring M, Santini S, Gupta A, Jain R. Content based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell. 2000;22(12):1349–80.

    Article  Google Scholar 

  11. Snoek C, Worring M. Multimodal video indexing: a review of the state-of-the-art. Multimed Tool Appl. 2005;25(1):5–35.

    Article  Google Scholar 

  12. Snoek C, Worring M, van Gemert JC, Geusebroek JM, Smeulders A. The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of the 14th ACM International Conference on Multimedia; 2006.

    Google Scholar 

  13. Snoek C, Worring M, Geusebroek J, Koelma D, Seinstra F, Smeulders A. The semantic pathfinder: using an authoring metaphor for generic multimedia indexing. IEEE Trans Pattern Analy Mechine Intell. 2006;28(10):1678–89.

    Article  Google Scholar 

  14. Vapnik V. The nature of statistical learning theory. New York: Springer; 2000.

    Book  MATH  Google Scholar 

  15. Yanagawa A, Chang SF, Kennedy L, Hsu W. Columbia university’s baseline detectors for 374 LSCOM semantic visual concepts. Columbia University, 2007. aDVENT technical report 222-2006-8.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring & Cees Snoek

Authors
  1. Marcel Worring
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cees Snoek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcel Worring .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Dept. of Computer Science, New Jersey Inst. of Technology, Newark, NJ, USA

    Vincent Oria

  2. Digital Content and Media Sciences ReseaMultimedia Information Research Division, National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Worring, M., Snoek, C. (2018). Visual Content Analysis. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1019

Download citation

Publish with us

Policies and ethics

Search

Navigation