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Rotation-invariant features based on directional coding for texture classification

  • Farida Ouslimani
  • Achour Ouslimani
  • Zohra Ameur
Original Article
  • 38 Downloads

Abstract

A directional coding (DC) method is proposed to extract rotation-invariant features for texture classification. DC uses four orientations in \(3\times 3\) neighborhood pixel. For each orientation, the rank order of the central gray-level pixel is calculated. The four ranks are used to get 15 codes. The codes are combined with the information of the central pixel to extract 30 rotation-invariant features. For a multi-resolution study, DC is calculated by altering the window size around a central pixel. The number of samples is restricted to eight neighbors by local averaging. Therefore, in each single-scale DC histogram, the number of bins is kept small and constant. Outex, CUReT and KTH_TIPS2 databases are used to evaluate and compare the proposed method against some state-of-the-art local binary techniques and other texture analysis methods. The results obtained suggest that the proposed DC method outperforms other methods making it attractive for use in computer vision problems.

Keywords

Texture classification Rotation invariance Texture features Directional rank 

Notes

Compliance with ethical standards

Conflict of interest

No conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Farida Ouslimani
    • 1
  • Achour Ouslimani
    • 2
  • Zohra Ameur
    • 1
  1. 1.Laboratoire d’Analyse et de Modélisation des Phénomènes Aléatoires (LAMPA)Université Mouloud MammeriTizi OuzouAlgeria
  2. 2.Laboratoire Quartz,EA -7393 ENSEACergy-Pontoise CedexFrance

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