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首页> 外文期刊>Journal of Imaging >A Comparative Study of Two State-of-the-Art Feature Selection Algorithms for Texture-Based Pixel-Labeling Task of Ancient Documents
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A Comparative Study of Two State-of-the-Art Feature Selection Algorithms for Texture-Based Pixel-Labeling Task of Ancient Documents

机译：古代文献中基于纹理的像素标注任务的两种最新特征选择算法的比较研究

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Recently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest ( HBR2013 dataset : PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set.
机译：近来，纹理特征已被广泛用于历史文档图像分析。但是，很少有研究专门针对历史文档图像分析的特征选择算法。实际上，已经出现了在数据挖掘和机器学习任务中使用特征选择算法的重要需求，因为它有助于减少数据维数并提高算法性能，例如像素分类算法。因此，在本文中，我们使用基于分析和选择纹理特征的经典像素标记方案，提出了两种常规特征选择算法（遗传算法和ReliefF算法）的比较研究。在这项研究中，两种评估的特征选择算法已应用于HBR数据集的训练集，以便推论每个分析的基于纹理的特征集的最选择的纹理特征。在这项研究中，评估后的特征集包括许多最新的纹理特征（Tamura，局部二进制图案，灰度游程矩阵，自相关函数，灰度共现矩阵，Gabor滤波器，三级Haar小波变换，使用3-抽头Daubechies滤波器的三级小波变换和使用4-抽头Daubechies滤波器的三级小波变换）。在我们的实验中，使用了在历史图书识别竞赛（HBR2013数据集：PRImA，英国索尔福德）中提供的公共历史文献图像语料库。本研究中进行了定性和数值实验，以便针对使用的纹理特征集，针对每种评估特征选择算法的优缺点提供一套全面的指导原则。

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