  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.
