Image Thresholding Optimization Based on Imperialist Competitive Algorithm

This paper presents a new method for thresholding using an evolutionary algorithm for optimizing image threshold especially for object segmentation. This method is resistant versus harsh histograms because of suitable fitness function selecting that consists of the histogram details. Afterwards imperialist competitive algorithm is recomanded as an evolutionary method for minimizing the error function. Finally, a powerful algorithm for image thresholding is founded. The comparisons and experimental results show that this system is more efficiently toward other methods; particularly Otsu’s and even former evolutionary algorithms like GA.

Keywords:

component , Image Thresholding , ICA Algorithm , Fitness Function , Otsu , GA