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DUT-MMSR at MediaEval 2017: Predicting Media Interestingness Task

By Reza Reza Aditya Permadi, Septian Septian Gilang Permana Putra, Helmi Helmiriawan, C.C.S. Liem, Guillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler, Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth J.F. Jones and Martha Larson


<p>This paper describes our approach for the submission to the Media-eval 2017 Predicting Media Interestingness Task, which was particularlydeveloped for the Image subtask. An approach using a late fusion strategy is employed, combining classifiers from different features by stacking them using logistic regression (LR). As the task ground truth was based on pairwise evaluation of shots or keyframe images within the same movie, next to using precomputed features as-is, we also include a more contextual feature, considering aver-aged feature values over each movie. Furthermore, we also consider evaluation outcomes for the heuristic algorithm that yielded the highest MAPscore on the 2016 Image subtask. Considering results obtained for the development and test sets, our late fusion method shows consistent performance on the Image subtask, but not on the Video subtask. Furthermore, clear differences can be observed between MAP@10 and MAP scores.</p><p><br/></p

Year: 2017
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Provided by: NARCIS
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