KMeans clustering for Image Data
Neither, there is not enough discriminatory information in data (yet)

Dont squeeze the data until it tells you the truth. You can change the metric (malahobian distance for example) and the algo but you cant expect it to show miracles. 

Using elbow method, as you increase the number of clusters it will always become more homogenous. You dont have a "kink" indicating optimal clustering number.

And with Silhouete (be careful those are averaged Silhoute score per cluster, in other words for k=4 you have 4 scores indicating wether points are lying inside/border/should- be-other-cluster that are averaged) you get that all of the points (in average) lie on the border of the clusters, without clear distinction of the clusters (thats what 0 means).

Advice Find better quantitative representation of data, new feature, reduce noise etc...