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Classification of time-series images using deep convolutional neural networks
Author(s): Nima Hatami; Yann Gavet; Johan Debayle
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Paper Abstract

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Paper Details

Date Published: 13 April 2018
PDF: 8 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960Y (13 April 2018); doi: 10.1117/12.2309486
Show Author Affiliations
Nima Hatami, Ecole Nationale Supérieure des Mines de Saint-Étienne (France)
Yann Gavet, Ecole Nationale Supérieure des Mines de Saint-Étienne (France)
Johan Debayle, Ecole Nationale Supérieure des Mines de Saint-Étienne (France)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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