The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Classification of time-series images using deep convolutional neural networks
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
Recurrence plots for the analysis of complex systems
MARWAN, N., CARMENROMANO, M., THIEL, M., & KURTHS, J. (2007) · Physics Reports · doi:10.1016/j.physrep.2006.11.001 · Cited by 3,814
Recurrence Plots of Dynamical Systems
Eckmann, J.-P., Kamphorst, S. O., & Ruelle, D. (1987) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/4/9/004 · Cited by 3,284
(2015) · Cited by 989
(2009) · neural information processing systems · Cited by 962
Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition
Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., & Penn, G. (2012) · 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2012.6288864 · Cited by 876
Cited by
Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342
“…extraction method for hand engineering approaches is the transformation of time series into images using specific imaging methods such as Gramian fields (Wang and Oates, 2015b,a), recurrence plots (Hatami et al., 2017; Tripathy and Acharya, 2018) and Markov transition fields (Wang and Oates, 2015).”
MethodFink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103678 · Cited by 549
Self-attention for raw optical Satellite Time Series Classification
Rußwurm, M., & Körner, M. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.06.006 · Cited by 355
Mahmud, T., Sazzad Sayyed, A. Q. M., Fattah, S. A., & Kung, S.-Y. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3015781 · Cited by 58
Metadata
- Authors
- Debayle, J., Hatami, N., & Gavet, Y.
- Venue
- Tenth International Conference on Machine Vision (ICMV 2017)
- Publisher
- SPIE
- Published
- 2018-04-13
- Pages
- 23
- OpenAlex
- W2963304300
- Cited by
- 257
- References
- 32
- FWCI
- 27.04
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Classification of time-series images using deep convolutional neural networks. https://app.cochat.ai/papers/10.1117/12.2309486
- The paper
- Debayle, J., Hatami, N., & Gavet, Y. (2018). Classification of time-series images using deep convolutional neural networks. Tenth International Conference on Machine Vision (ICMV 2017). https://doi.org/10.1117/12.2309486
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
