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Rotation-invariant features based on directional coding for texture classification

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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.

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Authors and Affiliations

  1. Laboratoire d’Analyse et de Modélisation des Phénomènes Aléatoires (LAMPA), Université Mouloud Mammeri, Tizi Ouzou, Algeria

    Farida Ouslimani & Zohra Ameur

  2. Laboratoire Quartz,EA -7393 ENSEA, 6, avenue du ponceau, 95014, Cergy-Pontoise Cedex, France

    Achour Ouslimani

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  1. Farida Ouslimani
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  2. Achour Ouslimani
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  3. Zohra Ameur
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Correspondence to Farida Ouslimani.

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Ouslimani, F., Ouslimani, A. & Ameur, Z. Rotation-invariant features based on directional coding for texture classification. Neural Comput & Applic 31, 6393–6400 (2019). https://doi.org/10.1007/s00521-018-3462-9

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  • DOI: https://doi.org/10.1007/s00521-018-3462-9

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