Skip to main content
SpringerLink
Log in

Effectiveness of ICF Features for Collection-Specific CBIR

  • Conference paper
Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation (AMR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7836))

Included in the following conference series:

Abstract

This study aims to find more effective methods for collection-specific CBIR. A lot of work has been done in trying to adapt a system by user feedback, in this study we aim to adapt CBIR systems for specific image collections in an automated manner. Independent Component Analysis (ICA), a high order statistical technique, is used to extract Independent Component Filters (ICF) from image sets. As these filters are adapted to the data, the hypothesis is that they may provide features which are more effective for collection-specific CBIR. To test this question, this study develops a methodology to extract ICF from image sets and use them to extract filter responses. In developing this method, the study uses image cross-correlation and clustering to solve issues to do with shifted/duplicate filters and selecting a smaller set of filters to make CBIR practical. The method is used to generate filter responses for the VisTex database . The filter response energies are used as features in the GNU Image Finding Tool (GIFT). The experiments show that features extracted using ICF have the potential to improve the effectiveness of collection-specific CBIR, although some more work in this area is required.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
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. Bai, B., Kantor, P., Shokoufandeh, A., Silver, D.: fMRI brain image retrieval based on ICA components. In: Eighth Mexican International Conference on Current Trends in Computer Science, ENC 2007, September 24-28, pp. 10–17 (2007)

    Google Scholar 

  2. Bell, A.J., Sejnowski, T.J.: The “independent components” of natural scenes are edge filters. Vision Research 37(23), 3327–3338 (1997), http://dx.doi.org/10.1016/S0042-69899700121-1

    Article  Google Scholar 

  3. Borgne, H.L., Guerin-Dugue, A.: Sparse-dispersed coding and images discrimination with independent component analysis. In: Proceedings of the Third International Conference on Independent Component Analysis and Signal Separation, ICA 2001 (December 2001)

    Google Scholar 

  4. Guo, C., Wilson, C.: Use of self-organizing maps for texture feature selection in content-based image retrieval. In: IEEE International Joint Conference on Neural Networks, pp. 765–770 (June 2008)

    Google Scholar 

  5. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973), http://dx.doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  6. Hateren, J.H.V., Schaaf, A.V.D.: Independent component filters of natural images compared with simple cells in primary visual cortex. In: Proceedings of the Biological sciences/The Royal Society, vol. 265(1394) (1998)

    Google Scholar 

  7. Howarth, P., Yavlinsky, A., Heesch, D., Rüger, S.M.: Medical image retrieval using texture, locality and colour. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 740–749. Springer, Heidelberg (2005), http://dx.doi.org/10.1007/1151964572 ,

    Chapter  Google Scholar 

  8. Huang, B., Li, J., Hu, S.: Texture feature extraction using ICA filters. In: 7th World Congress on Intelligent Control and Automation, WCICA 2008, pp. 7631–7634 (June 2008)

    Google Scholar 

  9. Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley-Interscience (May 2001), http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/047140540X

  10. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999), http://dx.doi.org/10.1145/331499.331504

    Article  Google Scholar 

  11. Khaparde, A., Deekshatulu, B.L., Madhavilatha, M., Farheen, Z., Sandhya Kumari, V.: Content based image retrieval using independent component analysis. IJCSNS International Journal of Computer Science and Network Security 8(4), 327–332 (2008)

    Google Scholar 

  12. Le Borgne, H., Guérin-Dugué, A., Antoniadis, A.: Representation of images for classification with independent features. Pattern Recognition Letters 25(2), 141–154 (2004), http://dx.doi.org/10.1016/j.patrec.2003.09.011

    Article  Google Scholar 

  13. Li, Z., Wu, S., Wang, X., Ye, H., Wang, M., Ye, J.: Dimensional reduction based on independent component analysis for content based image retrieval. In: International Joint Conference on Artificial Intelligence, JCAI 2009, pp. 741–745 (2009)

    Google Scholar 

  14. Marques, O., Furht, B.: Content-Based Image and Video Retrieval. Kluwer Academic Publishers, Norwell (2002)

    Book  MATH  Google Scholar 

  15. Müller, H., Rosset, A., Vallee, J., Geissbuhler, A.: Comparing feature sets for content based image retrieval in a medical case database. In: Proceedings of the Medical Informatics Europe Conference, MIE 2003, St. Malo, France (May 2003)

    Google Scholar 

  16. Müller, W.: Design and implementation of a flexible Content–Based Image Retrieval Framework - The GNU Image Finding Tool. Ph.D. thesis, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland (September 2001)

    Google Scholar 

  17. Smoaca, A., Coltuc, D., Lazarescu, V.: Pattern segmentation in textile images. In: International Symposium on Signals, Circuits and Systems, ISSCS 2009, pp. 1–4 (July 2009)

    Google Scholar 

  18. Squire, D.M., Müller, W., Müller, H., Raki, J.: Content-based query of image databases, inspirations from text retrieval: inverted files, frequency-based weights and relevance feedback. Pattern Recognition Letters, 143–149 (1999)

    Google Scholar 

  19. Sun, G., Liu, J., Sun, J., Ba, S.: Locally salient feature extraction using ICA for content-based face image retrieval. In: International Conference on Innovative Computing, Information and Control, vol. 1, pp. 644–647 (2006)

    Google Scholar 

  20. Trojan, N.: CBIR-based Dermatology Diagnostic Assistant. Honours thesis, School of Computer Science and Software Engineering, Monash University, Clayton, Victoria, Australia (November 2004); supervised by Dr. David Squire

    Google Scholar 

  21. Wang, F., Dai, Q.-H.: A new multi-view learning algorithm based on ICA feature for image retrieval. In: Cham, T.-J., Cai, J., Dorai, C., Rajan, D., Chua, T.-S., Chia, L.-T. (eds.) MMM 2007. LNCS, vol. 4351, pp. 450–461. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  22. Zhang, R., Zhang, X.P., Guan, L.: Wavelet-based texture retrieval using independent component analysis, vol. 6, pp. VI-341–VI-344 (September 2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Clayton Campus, Wellington Road, Victoria, 3800, Australia

    Nabeel Mohammed & David McG. Squire

Authors
  1. Nabeel Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David McG. Squire
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Laboratoire d’Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie, 104, Avenue du Président Kennedy, 75016, Paris, France

    Marcin Detyniecki

  2. Departamento de Lenguajes y Sistemas Informáticos (LSI), Universidad Nacional de Educación a Distancia (UNED), 28040, Madrid, Spain

    Ana García-Serrano

  3. Faculty of Computer Science, Otto-von-Guericke University, Universitätsplatz 2, 39106, Magdeburg, Germany

    Andreas Nürnberger  & Sebastian Stober  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mohammed, N., Squire, D.M. (2013). Effectiveness of ICF Features for Collection-Specific CBIR. In: Detyniecki, M., García-Serrano, A., Nürnberger, A., Stober, S. (eds) Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation. AMR 2011. Lecture Notes in Computer Science, vol 7836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37425-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37425-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37424-1

  • Online ISBN: 978-3-642-37425-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation