Name
Title | ||
|---|---|---|
A Multi-Modal Mixed-State Dynamic Bayesian Network For Robust Meeting Event Recognition From Disturbed Data |
Abstract | ||
|---|---|---|
In this work we present a novel multi-modal mixed-state dynamic Bayesian network (DBN) for robust meeting event classification. The model uses information from lapel microphones, a microphone array and Visual information to structure meetings into segments. Within the DBN a multistream hidden Markov model (HMM) is coupled with a linear dynamical system (LDS) to compensate disturbances in the data. Thereby the HMM is used as driving input for the LDS. The model can handle noise and occlusions in all channels. Experimental results on real meeting data show that the new model is highly preferable to all single-stream approaches. Compared to a baseline multi-modal early Fusion HMM, the new DBN is more than 2.5%, respectively 1.5% better for clear and disturbed data, this corresponds to a relative error reduction of 17%, respectively 9%. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICME.2005.1521356 | 2005 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), VOLS 1 AND 2 |
Keywords | Field | DocType |
hidden markov model,relative error,dynamic bayesian network,hmm,image classification,ambient intelligence,robustness,hidden markov models,bayesian methods,linear dynamical system | Linear dynamical system,Pattern recognition,Computer science,Robustness (computer science),Speech recognition,Microphone array,Artificial intelligence,Contextual image classification,Hidden Markov model,Approximation error,Bayesian probability,Dynamic Bayesian network | Conference |
Citations | PageRank | References |
10 | 1.01 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc Al-Hames | 1 | 116 | 8.75 |
| Gerhard Rigoll | 2 | 2788 | 268.87 |