Multiple classifier combination using reject options and markov fusion networks
Proceedings of the 14th ACM international conference on Multimodal interaction, 2012•dl.acm.org
The audio/visual emotion challenge (AVEC) resembles a benchmarking data collection in
order to evaluate and develop techniques for the recognition of affective states. In our work,
we present a Markov fusion network (MFN) for the combination of different individual
classifiers, that is derived from the well-known Markov random fields (MRF). It is capable to
restore missing values from a sequence of decisions and can integrate multiple channels
and weights them dynamically using confidences. The approach shows promising challenge …
order to evaluate and develop techniques for the recognition of affective states. In our work,
we present a Markov fusion network (MFN) for the combination of different individual
classifiers, that is derived from the well-known Markov random fields (MRF). It is capable to
restore missing values from a sequence of decisions and can integrate multiple channels
and weights them dynamically using confidences. The approach shows promising challenge …
The audio/visual emotion challenge (AVEC) resembles a benchmarking data collection in order to evaluate and develop techniques for the recognition of affective states. In our work, we present a Markov fusion network (MFN) for the combination of different individual classifiers, that is derived from the well-known Markov random fields (MRF). It is capable to restore missing values from a sequence of decisions and can integrate multiple channels and weights them dynamically using confidences. The approach shows promising challenge results compared to the baseline.
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