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Neural Computing and Applications

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04-04-2018 | Original Article

Rotation-invariant features based on directional coding for texture classification

Authors: Farida Ouslimani, Achour Ouslimani, Zohra Ameur

Published in: Neural Computing and Applications | Issue 10/2019

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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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Title: Rotation-invariant features based on directional coding for texture classification
Authors: Farida Ouslimani
Achour Ouslimani
Zohra Ameur
Publication date: 04-04-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3462-9

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