Christlein V, Bernecker D, Hönig FT, Angelopoulou E (2014)
Publication Type: Conference contribution, Conference Contribution
Publication year: 2014
Publisher: IEEE Computer Society
Edited Volumes: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Pages Range: 998 - 1005
Conference Proceedings Title: Proceedings of the 2014 IEEE Winter Conference on Applications of Computer Vision
Event location: Steamboat Springs, CO
URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2014/Christlein14-WIA.pdf
DOI: 10.1109/WACV.2014.6835995
This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the CVL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors. © 2014 IEEE.
APA:
Christlein, V., Bernecker, D., Hönig, F.T., & Angelopoulou, E. (2014). Writer Identification and Verification Using GMM Supervectors. In Proceedings of the 2014 IEEE Winter Conference on Applications of Computer Vision (pp. 998 - 1005). Steamboat Springs, CO: IEEE Computer Society.
MLA:
Christlein, Vincent, et al. "Writer Identification and Verification Using GMM Supervectors." Proceedings of the 2014 IEEE Winter Conference on Applications of Computer Vision (WACV), Steamboat Springs, CO IEEE Computer Society, 2014. 998 - 1005.
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