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
themembership functions are optimized by genetic algorithm
after empirical design stage. 90.62 and 96.25%classification
rates for RGB and HSI color spaces confirm the reliability of
optimized system in different image conditions given in this
contribution.
