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.

翻译：在这项工作中,我们提出了一套用于病毒传输电子显微镜图像分类的描述词。我们提议将人工制作和深层学习的方法结合起来,用于病毒图像分类。手工制作的这套方法主要基于本地二进制模式变体,每个描述词都经过不同的辅助矢量机培训,然后将一组分类器结合成总则。深层学习方法是在图像网络上预先培训的密度网 201,然后在病毒数据集中进行调试,该网被用作向另一个辅助矢量机(特别是最后一个平均集合层)喂食的特征提取器。最后,对手工制作特征进行训练的分类器和对深层学习特征进行训练的分类器,通过总则加以结合。拟议的聚合法有力地推动了每种独立方法获得的性能,获得了艺术性能的状态。