• DocumentCode
    657999
  • Title

    Hybrid classifier using SIFT descriptor

  • Author

    Kabbai, Leila ; Abdellaoui, Mahmoud ; Douik, Ahmed

  • Author_Institution
    Nat. Eng. Sch. of Monastir-ENIM, Univ. of Monastir, Monastir, Tunisia
  • fYear
    2013
  • fDate
    6-8 May 2013
  • Firstpage
    388
  • Lastpage
    392
  • Abstract
    In this paper, three image classification methods based on Interest Points (IP) will be presented to identify different types of noise attack (Affine, Zoom, Blur and Contrast) between two images using SIFT descriptor. The first one is an affine classifier deduced from several test on a standard images database. The second is a fuzzy classifier based on rules deduced from evolution curves of membership functions. Using the results obtained from the two classifiers, we conceive a third one based on their combination and denoted as “hybrid classifier”. By analyzing the results of the three classifiers, we deduced that the last one leads to best results as we obtained a recognition rate exceeding 95%. This classifier will be considered as good solution to predict the noise attack type.
  • Keywords
    affine transforms; fuzzy set theory; image classification; Affine noise attack; Blur noise attack; Contrast noise attack; SIFT descriptor; Zoom noise attack; affine classifier; fuzzy classifier; hybrid classifier; image classification method; image database; interest points; membership function evolution curve; noise attack type; Databases; Educational institutions; Histograms; IP networks; Noise; Object recognition; Standards; Classifier; SIFT; interest points; matching; noise attack;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Decision and Information Technologies (CoDIT), 2013 International Conference on
  • Conference_Location
    Hammamet
  • Print_ISBN
    978-1-4673-5547-6
  • Type

    conf

  • DOI
    10.1109/CoDIT.2013.6689576
  • Filename
    6689576