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Computing Multi-purpose Image-Based Descriptors for Object Detection: Powerfulness of LBP and Its Variants

  • Karim HammoudiEmail author
  • Mahmoud Melkemi
  • Fadi Dornaika
  • Tran Duy Anh Phan
  • Oussama Taoufik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)

Abstract

In this paper, we present some powerful methods for computing multi-purpose image-based descriptors toward their exploitations in object detection and recognition applications. Image-based descriptors characterize image properties for constituting machine learning-based recognition systems. In this context, we present the principle for computing image-based descriptors using the local binary pattern (LBP) method. Such a method is multi-purpose in the sense that it can be efficiently exploited for the automated recognition of objects of varied natures (e.g., vehicles of traffic-monitoring images, cells of medical images). Then, we propose three variants of LBP, named Mean-LBP, \(\lambda \)-2RLBP, and C2R-LBP. The two latter ones use Hamming distance. Experimental results show that our method can overpass performances of discussed LBP methods notably under realistic conditions of use.

Keywords

Object recognition system Image analysis Machine learning Local binary pattern 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Karim Hammoudi
    • 1
    • 2
    Email author
  • Mahmoud Melkemi
    • 1
    • 2
  • Fadi Dornaika
    • 3
    • 4
  • Tran Duy Anh Phan
    • 1
  • Oussama Taoufik
    • 1
  1. 1.Department of Computer ScienceUniversité de Haute-Alsace, IRIMAS, LMIA (EA 3993), MAGEMulhouseFrance
  2. 2.Université de StrasbourgStrasbourgFrance
  3. 3.Department of Computer Science & Artificial IntelligenceUniversity of the Basque CountrySan SebastiànSpain
  4. 4.IKERBASQUE, Basque Foundation for ScienceBilbaoSpain

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