Digital image classification by optimised fuzzy system
Image classification is a challenging problem of computer vision. This study reports a fuzzy system to semantic image classification. As it is a complex task, various information of digital image, including: three color space components and two Zernike moments with different order are gathered and u...
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| Main Authors: | , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2019-06-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| Summary: | Image classification is a challenging problem of computer vision. This study reports a fuzzy system to semantic image classification. As it is a complex task, various information of digital image, including: three color space components and two Zernike moments with different order are gathered and utilized as an input of fuzzy inference system to materialize a robust rotation/lighting condition and size invariant image classifier. For better performance, all the membership functions are optimized by genetic algorithm after empirically design stage. 93.07% and 95.25% classification rates empirically design and optimized systems confirm the reliability of proposed method in different image conditions given in this contribution. |
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| Item Description: | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/15171 |