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Book Sections Year : 2016

Dense Bag-of-Temporal-SIFT-Words for Time Series Classification

Abstract

The SIFT framework has shown to be effective in the image classification context. In [4], 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 paper, 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 publicly available UCR datasets.
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Dates and versions

hal-01252726 , version 1 (08-01-2016)
hal-01252726 , version 2 (12-01-2016)
hal-01252726 , version 3 (24-05-2016)
hal-01252726 , version 4 (25-05-2016)

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Cite

Adeline Bailly, Simon Malinowski, Romain Tavenard, Laetitia Chapel, Thomas Guyet. Dense Bag-of-Temporal-SIFT-Words for Time Series Classification. Advanced Analysis and Learning on Temporal Data, Springer, 2016, 978-3319444116. ⟨10.1007/978-3-319-44412-3_2⟩. ⟨hal-01252726v4⟩
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