Section: New Results
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification
Participants : Adeline Bailly [IRISA/Obelix] , Laetitia Chapel [IRISA/Obelix] , Thomas Guyet, Simon Malinowski [LinkMedia] , Romain Tavenard [IRISA/Obelix] .
The SIFT framework has shown to be effective in the image classification context. In , we designed a Bag-of-Words approach based on an adaptation of this framework to time series classification. It relies on two steps: SIFT-based features are first extracted and quantized into words; histograms of occurrences of each word are then fed into a classifier. In this work, we investigate techniques to improve the performance of Bag-of-Temporal-SIFT-Words: dense extraction of keypoints and different normalizations of Bag-of-Words histograms. Extensive experiments show that our method significantly outperforms nearly all tested standalone baseline classifiers on UCR datasets.