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Content Provider | IEEE Xplore Digital Library |
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Author | Mandun Zhang Na Lu Ming Yu Xuefeng Zhou |
Copyright Year | 2009 |
Abstract | A new method using feature subspace for writer identification is proposed in this paper. The current writer identification algorithms are always that the more the extraction features the better the classifier result. However it will result in excessive calculational cost on classifier identification process. On the basis of these issues, after we obtain the high-dimensional features, first we extract the more useful features to compose of the subspace, and then carry on the identification process. It shows that this new method, compared with the classical method, not only achieves better identification results but also greatly reduces the elapsed time on computation of the identification process. |
Starting Page | 316 |
Ending Page | 318 |
File Size | 253600 |
Page Count | 3 |
File Format | |
ISBN | 9781424455577 |
DOI | 10.1109/ICINIS.2009.87 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-11-01 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Algorithm design and analysis Text analysis Machine learning algorithms subspace Pattern recognition Image texture analysis Intelligent networks Image analysis feature feature extraction writer identification classifier Feature extraction Frequency Gabor filters |
Content Type | Text |
Resource Type | Article |
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