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Pyramid-Based Multi-structure Local Binary Pattern for Texture Classification

机译:基于金字塔的多结构局部二值图案用于纹理分类

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Recently, the local binary pattern (LBP) has been widely used in texture classification. The conventional LBP methods only describe micro structures of texture images, such as edges, corners, spots and so on, although many of them show a good performance on texture classification. This situation still could not be changed, even though the multiresolution analysis technique is used in methods of local binary pattern. In this paper, we investigate the drawback of conventional LBP operators in describing some textures that has the same small structures but differential large structures. And a multi-structure local binary pattern operator is achieved by executing the LBP method on different layers of image pyramid. The proposed method is simple yet efficient to extract not only the micro structures but also the macro structures of texture images. We demonstrate the performance of our method on the task of rotation invariant texture classification. The experimental results on Outex database show advantages of the proposed method.
机译:近来,局部二值模式(LBP)已被广泛地用于纹理分类。常规的LBP方法仅描述纹理图像的微观结构,例如边缘,拐角,斑点等,尽管其中许多在纹理分类方面显示出良好的性能。即使在局部二进制模式的方法中使用了多分辨率分析技术,这种情况仍然无法改变。在本文中,我们研究了传统LBP算子在描述某些具有相同小结构但具有差异大结构的纹理时的缺点。通过在图像金字塔的不同层上执行LBP方法,实现了多结构局部二进制模式算子。所提出的方法简单而有效,不仅可以提取纹理图像的微观结构,而且可以提取其宏观结构。我们证明了我们的方法在旋转不变纹理分类任务上的性能。在Outex数据库上的实验结果表明了该方法的优势。

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