Feature Extractor for the Classification of Approved Halal Logo in Malaysia
This paper present a new feature extractors called the Fractionalized Principle Magnitude (FPM) that is evaluated in the classification of approved Halal logo with respect to classification accuracy and time consumptions. Feature can be classified into two group; global feature and local feature....
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| Main Author: | |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/8550/1/06487196.pdf http://eprints.utem.edu.my/id/eprint/8550/ http://ieeexplore.ieee.org.libproxy.utem.edu.my/search/searchresult.jsp?newsearch=true&queryText=Feature+Extractor+for+the+Classification+of+Approved+Halal+Logo+in+Malaysia&x=-1003&y=-276 |
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| Summary: | This paper present a new feature extractors called the
Fractionalized Principle Magnitude (FPM) that is evaluated in the classification of approved Halal logo with respect to
classification accuracy and time consumptions. Feature can be classified into two group; global feature and local feature. In this study, several feature extractors have been compared with the proposed method such as histogram of gradient (HOG), Hu moment, Zernike moment and wavelet co-occurrence histogram (WCH). The experiments are conducted on 50 different approved Halal logos. The result shows that proposed FPM method achieves the highest accuracy with 90.4% whereas HOG, Zernike moment, WCH and Hu moment achieve 75.2%, 64.4%, 47.2% 44.4% of accuracies, respectively. |
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