Geometrical Feature Based Ranking using Grey Relational Analysis (GRA) for Writer Identification
The author’s unique characteristic is determined by the variation of generated features from feature extraction process. Different sets of features produced are based on different feature extraction methods (local or global). Thus, the process has led to the production of high dimensional datase...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/11929/1/socpar13_intan.pdf http://eprints.utem.edu.my/id/eprint/11929/ |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | The author’s unique characteristic is determined by
the variation of generated features from feature extraction
process. Different sets of features produced are based on
different feature extraction methods (local or global). Thus, the
process has led to the production of high dimensional datasets
that contributing to many irrelevant or redundant features.
The main problem however is to find a way to identify the most
significant features. The features ranking method using Grey
Relational Analysis (GRA) is proposed to find the significance
of each feature and give ranking to the features. This study
presents the Higher-Order United Moment Invariant (HUMI)
as the global feature extraction methods. The combinations of features with the higher ranking are discretized and used as the subsets of features to identify the writer. The result demonstrates that the average classification accuracy of five classifiers by using just the combination of two most significant features have yielded a better performance than using all features. |
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