A New Clustering Algorithm for Noisy Image Retrieval

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 110)


The paper concerns an open problem in the area of content based image retrieval (CBIR) and presents an original method for noisy image data sets by applying an artificial immune system model. In this regard, appropriate feature extraction methods in addition to a beneficial similarity criterion contribute to retrieving images from a noisy data set precisely. The results show some improvement and resistance in the noise tolerance of content based image retrieval in a database of various images.


Artificial immune system Content based noisy image retrieval, Fuzzy linking histogram Similarity criterion 


  1. 1.
    Dimov D, Marinov A (2006) Geometric-morphological method for artifact noise isolation in the image periphery by a contour evolution tree. In: International conference on computer systems and technologies—CompSysTech, Institute of Information Technologies, BulgarianGoogle Scholar
  2. 2.
    Long F, Zhang Hj, Feng DD (2001) Fundamentals of content-based image retrieval.Google Scholar
  3. 3.
    Home Page of Fuzzy Image Segmentation, University of Waterloo, Content by: H.R. Tizhoosh,
  4. 4.
    Vijay Kumar VR, Manikandan S, Ebenezer D, Vanathi PT, Kanagasabapathy P (2007) High density impulse noise removal in color images using median controlled adaptive recursive weighted median filter. IAENG Int J Comput Sci 14(1):31–34Google Scholar
  5. 5.
    Cohen I (2000) Tending Adam’s garden. London NW1 7DX,UkGoogle Scholar
  6. 6.
    Nasraoui O, Cardona C, Rojas C, Gonzalez F (2003) Mining evolving user profiles in noisy web clickstream data with a scalable immune system clustering algorithm. In: Proc. of WebKDD 2003–KDD Workshop on Web mining as a Premise to Effective and Intelligent Web Applications, Washington DC, p 71Google Scholar
  7. 7.
    Wei D, Zhan-sheng L, Xiaowei W (2007) Application of image recognition based on artificial immune in rotating machinery fault diagnosis. IEEE, WuhanGoogle Scholar
  8. 8.
    Konstantinidis K, Gasteratos A, Andreadis I (2005) Image retrieval based on fuzzy color histogram processing Opt Commun 248:375–386Google Scholar
  9. 9.
    Analoui M, Beheshti M (2011) Content-based image retrieval using artificial immune system (AIS) clustering algorithms, lecture notes in engineering and computer science: proceedings of the international multiconference of engineers and computer scientists 2011, IMECS 2011, Hong Kong, 16–18 March 2011Google Scholar
  10. 10.
    Zimmerman HJ (1987) Fuzzy sets, decision making and expert systems. Kluwer Academic, BostonCrossRefGoogle Scholar
  11. 11., MATLAB and Simulink for Technical Computing (2008)
  12. 12.
    Wikipedia-Image noise, the free encyclopedia,
  13. 13.
    Nasraoui O, Uribe CC, Coronel CR (2003) Tecno-streams: tracking evolving clusters in noisy data streams with a scalable immune system learning model. IEEE computer society, MelbourneGoogle Scholar
  14. 14.
    Nasraoui O, Rojas C (2003) From static to dynamic web usage mining: towards scalable profiling and personalization with evolutionary computation. Invited Paper in Workshop on Information Technology, Rabat, Morocco, March 2003Google Scholar
  15. 15.
    Analoui M, Beheshti M, Mahmoudi MT, Jadidi Z (2010) Tecno-streams approach for content-based image retrieval. In: Proceedings of the world congress on nature and biologically inspired computing, IEEE, FukuokaGoogle Scholar
  16. 16.
    Nasaroui O, Gonzalez F, Dasgupta D (2002) The fuzzy artificial immune system: motivations, basic concepts, and application to clustering and web profiling. IEEE, HonoluluGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.School of Computer EngineeringIran University of Science & TechnologyTehranIran
  2. 2.University of Science and Technology (IUST)TehranIran
  3. 3.University of Science and Technology (IUST)TehranIran

Personalised recommendations