I have a reference image A and 2 target images B and C , I tried to measure the SSIM as follows :

(from a human vision perception A & B are from the same class) and A & C from different class.

result1 = SSIM(A , B) = 4.71027%;  
result2 = SSIM(A , C) =  7.95047%;

I used the code from opencv : SSIM CODE

I also tried LBP normalized histogram of the entire image by calculating KL divergence of the two histograms, but the results were worst.

Is there a way to measure the similarity without training?

Image A :

Image B :

Image C :

EDIT :

After @Cris Luengo suggestion, these are the results of 2 LBP versions Circular, and Variance-based. It' seems like the choice of the method (features descriptor) is critical: (result = 0 means identical)

result1 = LPB_CIRCULAR_HIST_KL(A , B) =  0.66;
result2 = LPB_CIRCULAR_HIST_KL(A , C) =  0.64;

result1 = LPB_VAR_HIST_KL(A , B) = 0.49;
result2 = LPB_VAR_HIST_KL(A , C) = 3.74;