Will the features in the image (edge, color, etc.. ) impacts on the performance of the spherical k-means?
Yes it is true that in these images follow the trend you pointed, but that is not the only reason for the drop in accuracy:


Image 1 is pretty simple, and binary so it is easier samples, not because of clarity but of simplicity.
Image 2 is a bit more complex, but it is still binary and can be learned easily.
Image 3 is simple, but it is noisy and (as you pointed out) does not have strong features, since you're not dealing with CNNs you should pre-process this kind of dataset by binarization or image sharpening after noise removal (if you do it before you will amplify noise). This probably was taken with bad light conditions.
Image 4 this is really complex image, it is on color and until now I couldn't figure out what it is lol


Well, you are not wrong in your idea, but taking conclusions that way may mislead you on the future. There are other reasons (as pointed above) for your findings. So:


Create a thesis (like you did)
Test your thesis by trying to change the observed phenomena (e.g. pre-process images to find stronger features)
Check if that improved your results, create a new thesis and repeat until satisfied with results