Lexing Xie, Lyndon Kennedy, Shih-Fu Chang, Ajay Divakaran, Huifang Sun, Ching-Yung Lin. Layered Dynamic Mixture Model for Pattern Discovery in Asynchronous Multi-modal Streams. In Interational Conference on Acoustic, Speech and Signal Processing (ICASSP), Philadelphia, PA, March 2005.
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We propose a layered dynamic mixture model for asynchronous multi-modal fusion for unsupervised pattern discovery in video. The lower layer of the model uses generative temporal structures such as a hierarchical hidden Markov model to convert the audio-visual streams into mid-level labels, it also models the correlations in text with probabilistic latent semantic analysis. The upper layer fuses the statistical evidence across diverse modalities with a flexible meta-mixture model that assumes loose temporal correspondence. Evaluation on a large news database shows that multi-modal clusters have better correspondence to news topics than audio-visual clusters alone; novel analysis techniques suggest that meaningful clusters occur when the prediction of salient features by the model concurs with those shown in the story clusters
[ xie04discover ]
Lexing Xie 
Lyndon Kennedy
Shih-Fu Chang
Ching-Yung Lin
@InProceedings{xie05layered,
Author = {Xie, Lexing and Kennedy, Lyndon and Chang, Shih-Fu and Divakaran, Ajay and Sun, Huifang and Lin, Ching-Yung},
Title = {Layered Dynamic Mixture Model for Pattern Discovery in Asynchronous Multi-modal Streams},
BookTitle = {Interational Conference on Acoustic, Speech and Signal Processing (ICASSP)},
Address = {Philadelphia, PA},
Month = {March},
Year = {2005}
}
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