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Virus image classification using multi-scale completed local binary pattern features extracted from filtered images by multi-scale principal component analysis

机译：使用多尺度主成分分析从过滤后的图像中提取的多尺度完整局部二进制特征特征进行病毒图像分类

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摘要

Virus image classification is an important issue in clinical virology, highly accurate algorithm of automatic virus image classification is very helpful. In this paper, instead of extracting virus feature from the original image, we propose a novel method that extracts the virus feature from the filtered images by multi-scale principal component analysis (PCA). Firstly, multi-scale PCA filters are learned from all original images in the data set. Secondly, the original images are convolved with the learned filters. Therefore, the filtered images can capture the principal texture information from different perspectives. Then, the completed local binary pattern (CLBP) descriptor is firstly utilized to depict the features of all filtered virus images. The multi-scale CLBP features extracted from filtered images by multi-scale PCA are combined as the feature MPMC (Multi-scale PCA and Multi-scale CLBP), which is proposed in this paper. Finally, support vector machine (SVM) with polynomial kernel is used for classification. Experiments show that the classification accuracy based on MPMC outperforms the previous methods in the literature for the same virus image data set. (C) 2016 Elsevier B.V. All rights reserved.
机译：病毒图像分类是临床病毒学中的重要问题，高精度的自动病毒图像分类算法非常有帮助。在本文中，我们提出一种新颖的方法，而不是从原始图像中提取病毒特征，而是通过多尺度主成分分析（PCA）从过滤后的图像中提取病毒特征。首先，从数据集中的所有原始图像中学习多尺度PCA滤波器。其次，将原始图像与学习的滤波器进行卷积。因此，滤波后的图像可以从不同的角度捕获主要纹理信息。然后，首先利用完整的本地二进制模式（CLBP）描述符来描述所有过滤的病毒图像的特征。本文将多尺度PCA从滤波图像中提取的多尺度CLBP特征组合为特征MPMC（多尺度PCA和多尺度CLBP）。最后，使用支持多项式核的支持向量机（SVM）进行分类。实验表明，基于MPMC的分类精度优于文献中相同病毒图像数据集的分类精度。 （C）2016 Elsevier B.V.保留所有权利。

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