Multimodal Integration for Meeting Group Action Segmentation and Recognition
| Type of publication: | Conference paper |
| Citation: | zhang-rr-05-31b |
| Booktitle: | MLMI |
| Year: | 2005 |
| Note: | IDIAP-RR 05-31 |
| Crossref: | zhang-rr-05-31: |
| 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 four 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. |
| Userfields: | ipdmembership={vision}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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