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Texture features based on an efficient local binary pattern descriptor
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DOI：10.1016/j.compeleceng.2017.08.009.png)
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Abstract
En 中文
Texture characterization aims at describing the spatial arrangement of local structures within an image. However, mixed pixels that are generally located near boundaries of the regions represent challenge to perform accurate image texture discrimination. To address this problem, this paper proposes a robust discriminating texture features relying on an efficient Local Binary Pattern (LBP) descriptor, where the spatial information within image is taken into account. To determine for each pixel both a proper scale parameter and a threshold value to compute the LBP code, an efficient way relying on bilateral filter-based multi-scale image analysis is used. First, the difference of Gaussian operator is used to determine the corresponding scale. Second, key points based-approach is used to identify the threshold value of each pixel. This provides the ability to deal with mixed pixels. Then, LBP code is computed to characterize the texture information for each pixel. Experimental results, using both synthetic and real images, show that the proposed appropriate-scale-threshold selection strategy demonstrates a significant improvement in texture discrimination ability. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Texture discrimination
Multi-scale representation
Bilateral filter
Keypoints extraction
Scale invariant feature transform
Mixed pixels
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