Detecting Low-Quality Reference Time Series in Stream Recognition

Marc Dupont 1 Pierre-François Marteau 1 Nehla Ghouaiel 2
1 EXPRESSION - Expressiveness in Human Centered Data/Media
UBS - Université de Bretagne Sud, IRISA-D6 - MEDIA ET INTERACTIONS
2 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : On-line supervised spotting and classification of subsequences can be performed by comparing some distance between the stream and previously learnt time series. However, learning a few incorrect time series can trigger disproportionately many false alarms. In this paper, we propose a fast technique to prune bad instances away and automatically select appropriate distance thresholds. Our main contribution is to turn the ill-defined spotting problem into a collection of single well-defined binary classification problems, by segmenting the stream and by ranking subsets of instances on those segments very quickly. We further demonstrate our technique's effectiveness on a gesture recognition application.
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Marc Dupont, Pierre-François Marteau, Nehla Ghouaiel. Detecting Low-Quality Reference Time Series in Stream Recognition. International Conference on Pattern Recognition (ICPR), IAPR, Dec 2016, Cancun, Mexico. ⟨hal-01435197⟩

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