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Survey on LBP based texture descriptors for image classification

机译:基于LBP的纹理描述符的图像分类研究

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摘要

The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQ_P) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets. As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases.
机译:这项工作的目的是找到使用基于本地二进制模式(LBP)的方法描述给定纹理的最佳方法。首先对几种不同的方法进行比较,然后在不同的数据集上测试最佳融合方法,然后将其与文献中提出的几种方法进行比较(为了公平比较,在可能的情况下,我们使用原始作者共享的代码)。我们的实验表明,基于均匀局部五进制模式(LQ_P)和旋转不变局部五进制模式的融合方法,其中基于方差执行了bin选择,并应用了邻域保留嵌入(NPE)特征变换,从而获得了一种执行方法在所有测试的数据集上都很好。作为分类器,我们测试了独立支持向量机(SVM)和SVM的随机子空间集合。我们比较了几个纹理描述符,并表明我们提出的方法与随机子空间集合相结合的性能优于其他最新技术。该结论基于使用六个基准数据库在多个领域中进行的广泛实验。

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