Feature Extraction Method for Classification of Approved Halal Logo in Malaysia using Fractionalized Principle Magnitude
This paper presents 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 groups that ar...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Science and Engineering Publishing Company
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/8557/1/EMR017.pdf http://eprints.utem.edu.my/id/eprint/8557/ http://www.seipub.org/emr/AllIssues.aspx |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | This paper presents 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 groups
that are global 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. Furthermore, two other databases also have been used that is traffic sign and Outex database. The accuracy performance and classification time are compared with FPM and other method. |
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