Article

Semantic image classification by genetic algorithm using optimised fuzzy system based on Zernike moments

Signal Image and Video Processing (impact factor: 0.56). 05/2012;

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 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.

0 0
 · 
0 Bookmarks
 · 
80 Views
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

96.25 % classification rates
 
complex task
 
computer vision
 
empirical design stage
 
genetic algorithm
 
HSI color spaces
 
Image classification
 
materialize
 
membership functions
 
semantic image classification
 
size invariant image classifier
 
utilized
 
Zernike moments