Skip to main content
SpringerLink
Log in

Image Categorization Based on Computationally Economic LAB Colour Features

  • Conference paper
Soft Computing Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 195))

Abstract

An easy to compute and small colour feature vector is introduced in this paper, as a tool to be used in the process of retrieval or classification of similarly coloured digital images from very large databases. A particular set of “ab” planes from the LAB colour system is used, along with a specific configuration of colour regions within them. The colour feature vector is low dimensional (only 96 components), computationally economic and performs very well on a carefully selected database of rose images, publicly available.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall International, Inc. (1989)

    Google Scholar 

  2. Fairchild, M.D.: Color and Image Appearance Models. Color Appearance Models, pp. 340–385. John Wiley and Sons (2005)

    Google Scholar 

  3. Ciobanu, A., Costin, M., Barbu, T.: Extraction of main colors from a digital color image. In: Proceedings of the International Multidisciplinary Scientific Geo-Conference SGEM 2010, Albena, Bulgaria, June 20-25, vol. 1, pp. 1069–1076 (2010)

    Google Scholar 

  4. Ciobanu, A., Costin, M.: Improved Color Analysis and Feature Extraction Method for Digital Images. In: 6th European Conference on Intelligent Systems and Technologies, ECIT 2010, Iasi, Romania (October 2010)

    Google Scholar 

  5. Barbu, T., Ciobanu, A., Costin, M.: Unsupervised Color-Based Image Recognition using a LAB Feature Extraction Technique, Buletinul Institutului Politehnic Iaşi, Universitatea Tehnică “Gheorghe Asachi”, Tom LVII (LXI), Fasc. 3, Secţia Automatică şi Calculatoare (2011)

    Google Scholar 

  6. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An Efficient K-Means Clustering Algorithm: Analysis and Implementation. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(7), 881–892 (2002)

    Article  Google Scholar 

  7. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. International Student Edition, Thomson (2008)

    Google Scholar 

  8. Schettini, R., Ciocca, G., Zuffi, S.: Indexing and Retrieval in Colour Image Databases. In: Lindsay, W., RonnierLuo, M. (eds.) Indexing and Retrieval in Colour Image Databases. ch. 10, pp. 183–213. John Wiley & Sons, England (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, The Romanian Academy, Iasi, Romania

    Adrian Ciobanu, Mihaela Costin & Tudor Barbu

Authors
  1. Adrian Ciobanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mihaela Costin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tudor Barbu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrian Ciobanu .

Editor information

Editors and Affiliations

  1. , Department of Automation & Applied Infor, Aurel Vlaicu University of Arad, Blvd. Revolutiei 77, Arad, 310130, Romania

    Valentina Emilia Balas

  2. , Institute of Intelligent Engineering, Óbuda University, Bécsi út 96/b, Budapest, 1034, Hungary

    János Fodor

  3. Dept. Measurement &, Information Systems, Technical University Budapest, Magyar tudósok körutja 2, Budapest, 1521, Hungary

    Annamária R. Várkonyi-Kóczy

  4. , Department of Informatics, University of Szeged, Arpad ter 2., Szeged, 6720, Hungary

    Joszef Dombi

  5. , School of Electrical and Information, University of South Australia, 5095, Adelaide, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ciobanu, A., Costin, M., Barbu, T. (2013). Image Categorization Based on Computationally Economic LAB Colour Features. In: Balas, V., Fodor, J., Várkonyi-Kóczy, A., Dombi, J., Jain, L. (eds) Soft Computing Applications. Advances in Intelligent Systems and Computing, vol 195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33941-7_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33941-7_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33940-0

  • Online ISBN: 978-3-642-33941-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation