MediaEval 2018 Medico Task

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We participated in the MediaEval 2018 Medico task and recently submitted our working notes paper. This is joint work with Oge Marques (Florida Atlantic University, USA).

Update: Our paper has been accepted and presented at the MediaEval Workshop on Oct 30, 2018. The Workshop proceedings appeared at

Title: Early and Late Fusion of Classifiers for the MediaEval Medico Task

Authors: Mario Taschwer, Manfred Jürgen Primus, Klaus Schoeffmann, Oge Marques

Abstract: 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.

Paper: [Preprint PDF] [Official Workshop Paper]

Presentation: [Slides PDF]

Bibtex citation:

Title                    = {Early and Late Fusion of Classifiers for the {MediaEval Medico} Task},
Author                   = {Taschwer, Mario and Primus, Manfred J{\"u}rgen and Schoeffmann, Klaus and Marques, Oge},
Booktitle                = {Working Notes Proceedings of the MediaEval 2018 Workshop},
Year                     = {2018},
Editor                   = {M. Larson and P. Arora and C.H. Demarty and M. Riegler and B. Bischke and E. Dellandrea and M. Lux and A. Porter and G.J.F. Jones},
Series                   = {CEUR Workshop Proceedings},
Volume                   = {2283},
Url                      = {}