Multiple classifier combination using reject options and markov fusion networks

Authors:
Michael Glodek;Martin Schels;Günther Palm;Friedhelm Schwenker
Affiliations:
University of Ulm, Ulm, Germany;University of Ulm, Ulm, Germany;University of Ulm, Ulm, Germany;University of Ulm, Ulm, Germany
Venue:
Proceedings of the 14th ACM international conference on Multimodal interaction
Year:
2012

Citing 11
Cited 0

Fundamentals of speech recognition

Fundamentals of speech recognition
Comparison of different implementations of MFCC

Journal of Computer Science and Technology
Random Forests

Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Similarity-based clustering of sequences using hidden Markov models

MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
RASTA-PLP speech analysis technique

ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
AVEC 2011-the first international audio/visual emotion challenge

ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Incorporating uncertainty in a layered HMM architecture for human activity recognition

J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
To reject or not to reject: that is the question-an answer in caseof neural classifiers

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On optimum recognition error and reject tradeoff

IEEE Transactions on Information Theory
AVEC 2012: the continuous audio/visual emotion challenge - an introduction

Proceedings of the 14th ACM international conference on Multimodal interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.