Early and Late Fusion of Classifiers for the MediaEval Medico Task

In this paper we present our results for the MediaEval 2018 Medico task, achieved with traditional machine learning methods, such as logistic regression, support vector machines, and random forests. Before classification, we combine traditional global image features and CNN-based features (early fusion), and apply soft voting for combining the output of multiple classifiers (late fusion). Linear support vector machines turn out to provide both good classification performance and low run-time complexity for this task.