论文标题
病毒图像分类的深度学习和手工制作的功能
Deep learning and hand-crafted features for virus image classification
论文作者
论文摘要
在这项工作中，我们提出了一个描述符，以分类病毒的透射电子显微镜图像。我们建议将手工制作的深度学习方法结合在一起，以进行病毒图像分类。一组手工制作的主要基于本地二进制模式变体，对于每个描述符，都训练了不同的支持向量机，然后将一组分类器组合在一起。深度学习方法是在ImageNet上预处理的Densenet201，然后在病毒数据集中进行调整，将NET用作提取器的特征提取器，以供应另一台支持向量机，特别是最后一个平均池层用作特征提取器。最后，经过手工制作的功能培训的分类器和经过深度学习功能培训的分类器的分类器结合了总规则。提出的融合强烈提高了每种独立方法获得的性能，从而获得了最先进的表现。
In this work, we present an ensemble of descriptors for the classification of transmission electron microscopy images of viruses. We propose to combine handcrafted and deep learning approaches for virus image classification. The set of handcrafted is mainly based on Local Binary Pattern variants, for each descriptor a different Support Vector Machine is trained, then the set of classifiers is combined by sum rule. The deep learning approach is a densenet201 pretrained on ImageNet and then tuned in the virus dataset, the net is used as features extractor for feeding another Support Vector Machine, in particular the last average pooling layer is used as feature extractor. Finally, classifiers trained on handcrafted features and classifier trained on deep learning features are combined by sum rule. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.
