Deep Learning and Handcrafted Features for Virus Image Classification
Loris Nanni, Eugenio De Luca, Marco Ludovico Facin, Gianluca Maguolo
Abstract
In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.
Topics & Concepts
Artificial intelligenceComputer scienceDeep learningPattern recognition (psychology)Support vector machineFeature (linguistics)Artificial neural networkContextual image classificationFeature extractionDeep neural networksFusionImage (mathematics)Convolutional neural networkMachine learningFeature vectorEnsemble learningState (computer science)Statistical classificationTraining setBasis (linear algebra)Transmission (telecommunications)Computer visionCell Image Analysis TechniquesDomain Adaptation and Few-Shot LearningDigital Media Forensic Detection