Digital image classification by optimised fuzzy system

Mehdi Ramezanifard, B. S. Mousavi

Abstract


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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DOI: http://doi.org/10.11591/ijeecs.v14.i3.pp1196-1202

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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