Multimodal integration for meeting group action segmentation and recognition

Marc Al-Hames; Alfred Dielmann; Daniel Gatica-Perez; Stephan Reiter; Steve Renais; Gerhard Rigoll; Pong Zhang

doi:10.1007/11677482_5

Multimodal integration for meeting group action segmentation and recognition

Marc Al-Hames, Alfred Dielmann, Daniel Gatica-Perez, Stephan Reiter, Steve Renais, Gerhard Rigoll, Pong Zhang

Chair of Human-Machine Communication

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

We address the problem of segmentation and recognition of sequences of multimodal human interactions in meetings. These interactions can be seen as a rough structure of a meeting, and can be used either as input for a meeting browser or as a first step towards a higher semantic analysis of the meeting. A common lexicon of multimodal group meeting actions, a shared meeting data set, and a common evaluation procedure enable us to compare the different approaches. We compare three different multimodal feature sets and our modelling infrastructures: a higher semantic feature approach, multi-layer HMMs, a multi-stream DBN, as well as a multi-stream mixed-state DBN for disturbed data.

Original language	English
Title of host publication	Machine Learning for Multimodal Interaction - Second International Workshop, MLMI 2005, Revised Selected Papers
Pages	52-63
Number of pages	12
DOIs	https://doi.org/10.1007/11677482_5
State	Published - 2006
Event	2nd International Workshop on Machine Learning for Multimodal Interaction, MLMI 2005 - Edinburgh, United Kingdom Duration: 11 Jul 2005 → 13 Jul 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3869 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Machine Learning for Multimodal Interaction, MLMI 2005
Country/Territory	United Kingdom
City	Edinburgh
Period	11/07/05 → 13/07/05

Access to Document

10.1007/11677482_5

Cite this

Al-Hames, M., Dielmann, A., Gatica-Perez, D., Reiter, S., Renais, S., Rigoll, G., & Zhang, P. (2006). Multimodal integration for meeting group action segmentation and recognition. In Machine Learning for Multimodal Interaction - Second International Workshop, MLMI 2005, Revised Selected Papers (pp. 52-63). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3869 LNCS). https://doi.org/10.1007/11677482_5

Al-Hames, Marc ; Dielmann, Alfred ; Gatica-Perez, Daniel et al. / Multimodal integration for meeting group action segmentation and recognition. Machine Learning for Multimodal Interaction - Second International Workshop, MLMI 2005, Revised Selected Papers. 2006. pp. 52-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{576a4a344248433fbd2f995d28ea995e,

title = "Multimodal integration for meeting group action segmentation and recognition",

abstract = "We address the problem of segmentation and recognition of sequences of multimodal human interactions in meetings. These interactions can be seen as a rough structure of a meeting, and can be used either as input for a meeting browser or as a first step towards a higher semantic analysis of the meeting. A common lexicon of multimodal group meeting actions, a shared meeting data set, and a common evaluation procedure enable us to compare the different approaches. We compare three different multimodal feature sets and our modelling infrastructures: a higher semantic feature approach, multi-layer HMMs, a multi-stream DBN, as well as a multi-stream mixed-state DBN for disturbed data.",

author = "Marc Al-Hames and Alfred Dielmann and Daniel Gatica-Perez and Stephan Reiter and Steve Renais and Gerhard Rigoll and Pong Zhang",

year = "2006",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "2nd International Workshop on Machine Learning for Multimodal Interaction, MLMI 2005 ; Conference date: 11-07-2005 Through 13-07-2005",

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Al-Hames, M, Dielmann, A, Gatica-Perez, D, Reiter, S, Renais, S, Rigoll, G & Zhang, P 2006, Multimodal integration for meeting group action segmentation and recognition. in Machine Learning for Multimodal Interaction - Second International Workshop, MLMI 2005, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3869 LNCS, pp. 52-63, 2nd International Workshop on Machine Learning for Multimodal Interaction, MLMI 2005, Edinburgh, United Kingdom, 11/07/05. https://doi.org/10.1007/11677482_5

Multimodal integration for meeting group action segmentation and recognition. / Al-Hames, Marc; Dielmann, Alfred; Gatica-Perez, Daniel et al.
Machine Learning for Multimodal Interaction - Second International Workshop, MLMI 2005, Revised Selected Papers. 2006. p. 52-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3869 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We address the problem of segmentation and recognition of sequences of multimodal human interactions in meetings. These interactions can be seen as a rough structure of a meeting, and can be used either as input for a meeting browser or as a first step towards a higher semantic analysis of the meeting. A common lexicon of multimodal group meeting actions, a shared meeting data set, and a common evaluation procedure enable us to compare the different approaches. We compare three different multimodal feature sets and our modelling infrastructures: a higher semantic feature approach, multi-layer HMMs, a multi-stream DBN, as well as a multi-stream mixed-state DBN for disturbed data.

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Al-Hames M, Dielmann A, Gatica-Perez D, Reiter S, Renais S, Rigoll G et al. Multimodal integration for meeting group action segmentation and recognition. In Machine Learning for Multimodal Interaction - Second International Workshop, MLMI 2005, Revised Selected Papers. 2006. p. 52-63. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11677482_5

Multimodal integration for meeting group action segmentation and recognition

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