Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Techniques for Still Image Scene Classification and Object Detection

  • Conference paper
Artificial Neural Networks – ICANN 2006 (ICANN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

Included in the following conference series:

Abstract

In this paper we consider the interaction between different semantic levels in still image scene classification and object detection problems. We present a method where a neural method is used to produce a tentative higher-level semantic scene representation from low-level statistical visual features in a bottom-up fashion. This emergent representation is then used to refine the lower-level object detection results. We evaluate the proposed method with data from Pascal VOC Challenge 2006 image classification and object detection competition. The proposed techniques for exploiting global classification results are found to significantly improve the accuracy of local object detection.

Supported by the Academy of Finland in the projects Neural methods in information retrieval based on automatic content analysis and relevance feedback and Finnish Centre of Excellence in Adaptive Informatics Research.

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. ISO/IEC. Information technology - Multimedia content description interface - Part 3: Visual (2002) 15938-3:2002(E)

    Google Scholar 

  2. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  3. Koikkalainen, P.: Progress with the tree-structured self-organizing map. In: 11th European Conference on Artificial Intelligence. European Committee for Artificial Intelligence (ECCAI) (August 1994)

    Google Scholar 

  4. Koskela, M., Laaksonen, J., Sjöberg, M., Muurinen, H.: PicSOM experiments in TRECVID 2005. In: Proceedings of the TRECVID 2005 Workshop, Gaithersburg, MD, USA, pp. 262–270 (November 2005)

    Google Scholar 

  5. Laaksonen, J., Koskela, M., Oja, E.: PicSOM—Self-organizing image retrieval with MPEG-7 content descriptions. IEEE Transactions on Neural Networks, Special Issue on Intelligent Multimedia Processing 13(4), 841–853 (2002)

    Google Scholar 

  6. Ultsch, A.: Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series. In: Oja, E., Kaski, S. (eds.) Kohonen Maps, pp. 33–45. Elsevier, Amsterdam (1999)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, FIN-02015, TKK, Finland

    Ville Viitaniemi & Jorma Laaksonen

Authors
  1. Ville Viitaniemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorma Laaksonen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Viitaniemi, V., Laaksonen, J. (2006). Techniques for Still Image Scene Classification and Object Detection. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_4

Download citation

  • DOI: https://doi.org/10.1007/11840930_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation