Skip to main content

VISRET – A Content Based Annotation, Retrieval and Visualization Toolchain

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5807))

Abstract

This paper presents a system for content-based video retrieval, with a complete toolchain for annotation, indexing, retrieval and visualization of imported data. The system contains around 20 feature descriptors, a modular infrastructure for descriptor addition and indexing, a web-based search interface and an easy-to-use query-annotation-result visualization module. The features that make this system differ from others is the support of all the steps of the retrieval chain, the modular support for standard MPEG-7 and custom descriptors, and the easy-to-use tools for query formulation and retrieval visualization. The intended use cases of the system are content- and annotation-based retrieval applications, ranging from community video portals to indexing of image, video, judicial, and other multimedia databases.

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

Access this chapter

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. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)

    Article  Google Scholar 

  2. The Art Museum Image Consortium, http://www.amico.org/

  3. IBM’s Query by Image Content, IBM QBIC, http://wwwqbic.almaden.ibm.com/

  4. Chang, S.F., Chen, W., Meng, H.J., Sundaram, H., Zhong, D.: VideoQ: An Automatic Content-Based Video Search System Using Visual Cues. In: Proceedings of ACM Multimedia (1997)

    Google Scholar 

  5. Google video search, http://video.google.com/

  6. Yahoo video search, http://video.search.yahoo.com/

  7. Tineye, http://tineye.com/

  8. Jinni, http://www.jinni.com/

  9. Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: Color and Texture Descriptors. IEEE Tr. on Circuits and Systems for Video Technology 2(6), 703–715 (2001)

    Article  Google Scholar 

  10. Kovács, L., Szirányi, T.: Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest. IEEE Tr. on Pattern Analysis and Machine Intelligence 29(6), 1080–1085 (2007)

    Article  Google Scholar 

  11. Lowe, D.G.: Object recognition from local Scale-Invariant Features. In: Proceedings of ICCV, pp. 1150–1157 (1999)

    Google Scholar 

  12. Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Tr. on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)

    Article  Google Scholar 

  13. Burkhard, W., Keller, R.: Some Approaches to Best-Match File Searching. In: Proceedings of CACM (1973)

    Google Scholar 

  14. Viola, P., Jones, M.: Robust Real-Time Face Detection. International Journal of Computer Vision (IJCV) 57(2), 137–154 (2004)

    Article  Google Scholar 

  15. Kovács, L., Szirányi, T.: Evaluation of Relative Focus Map Based Image Indexing. In: Proceedings of CBMI, pp. 181–191 (2007)

    Google Scholar 

  16. Ion, A., Stanescu, L., Burdescu, D., Udristoiu, S.: Mapping Image Low-Level Descriptors to Semantic Concepts. In: Proceedings of ICCGI, pp. 154–159 (2008)

    Google Scholar 

  17. Fergus, R., Perona, P., Zisserman, A.: Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition. Int. J. Comput. Vision 71(3), 273–303 (2007)

    Article  Google Scholar 

  18. Annesley, J., Orwell, J., Renno, J.P.: Evaluation of MPEG7 Color Descriptors for Visual Surveillance Retrieval. In: Proceedings of ICCCN, pp. 105–112 (2005)

    Google Scholar 

  19. Ojala, T., Maenpaa, T., Viertola, J., Kyllonen, J., Pietikainen, M.: Empirical Evaluation of MPEG-7 Texture Descriptors with A Large-Scale Experiment. In: Proceeedings of the Workshop on Texture Analysis in Machine Vision, pp. 99–102 (2002)

    Google Scholar 

  20. Czúni, L., Hanis, A., Kovács, L., Kránicz, B., Licsár, A., Szirányi, T., Kas, I., Kovács, G., Manno, S.: Digital Motion Picture Restoration System for Film Archives (DIMORF). SMPTE Motion Imaging Journal, 170–178 (May/June 2004)

    Google Scholar 

  21. Friedman, J.H., Bentley, J.L., Finkel, R.A.: An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Trans. on Mathematical Software 3(3), 209–226 (1977)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kovács, L., Utasi, Á., Szirányi, T. (2009). VISRET – A Content Based Annotation, Retrieval and Visualization Toolchain. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04697-1_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04696-4

  • Online ISBN: 978-3-642-04697-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics