Threshold Learning from Samples Drawn from the Null Hypothesis for the Generalized Likelihood Ratio CUSUM Test

Author

Hory, C. ; Kokaram, A. ; Christmas, W.J.

Author_Institution

EEE Dept., Dublin Univ.

fYear

2005

fDate

28-28 Sept. 2005

Firstpage

111

Lastpage

116

Abstract

Although optimality of sequential tests for the detection of a change in the parameter of a model has been widely discussed, the test parameter tuning is still an issue. In this communication, we propose a learning strategy to set the threshold of the GLR CUSUM statistics to take a decision with a desired false alarm probability. Only data before the change point are required to perform the learning process. Extensive simulations are performed to assess the validity of the proposed method. The paper is concluded by opening the path to a new approach to multi-modal feature based event detection for video parsing

Keywords

feature extraction; learning (artificial intelligence); maximum likelihood estimation; probability; signal sampling; video signal processing; GLR CUSUM statistics; event detection; false alarm probability; generalized likelihood ratio CUSUM test; multimodal feature; null hypothesis; threshold learning; video parsing; Educational institutions; Event detection; Hidden Markov models; Performance evaluation; Probability; Sequential analysis; Signal processing; Speech processing; Streaming media; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2005 IEEE Workshop on

Conference_Location

Mystic, CT

Print_ISBN

0-7803-9517-4

Type

conf

DOI

10.1109/MLSP.2005.1532884

Filename

1532884