Table 2.

Algorithm for image segmentation using the HARIS technique.

Algorithm: HARIS algorithm for image segmentation
Start
Set the value of “max”
//Determine the centroid of the segment through, Sn=∑i=1max(intimax − μmax)(intimax − μmax)T
//Pixel assignment based on the Mahalanobis distance d2(int, pixn) stated as follows: d2(int, pixn)=(int − μi)intDx−1(int − μi)
//Assess the intraclass correlation determined by α, $\alpha =\frac{{\sigma }_i^2}{\left({\sigma }_i^2+{\sigma }_e^2/2\right)}$
//Assess the interclass variance determined by β, by assuming β=0 initially
for the value i = 0 to max-1, β=∑Cr=1maxωr(x)σr2(x)
end of for
//Implementing the first objective function-I
set the value of parameters like totpix, pixr, totcent, pixs, f(x)=(α × totpix/pixr)+(β × totcent/pixs)
return the value of f(x)
//Identify the fitness_spi, GBspfitness from the previous iteration
Assume max represents the sum of segments in the image
for the value i = 0 to max-1, spi=rand(0,1) × fitness_spi−1+rand(0,1) × (GBspfitness − fitnessspi−1), seg = evaluate (α, β)
return the value of spi and seg
end of for
End