@Zebrain I think you have a good amount of strength in your images that allow you to create an association. But I think the association only has meaning for these two items if you don’t plan to expand your knowledge of them. If I were to diagram it with my SEA-IT data types which are:
Ukraine would look like:
||a UK flag
|as a torero
||in a bullring
and Kiev would be
|like a bull
||in a bullring
What you notice is that you have most of the basic four types covered when you create your merged image which is really a short narrative with the two. The enhancements to each data type are all there except for the item enhancement of the UK flag. Is it big, small, heavy, twinkling with lights, etc?
Also, you notice that the two form a scene where they share a terrain or location. That probably isn’t going to happen separately.
One thing I think you need in case you plan to associate more items to each country is a standard way to visualize a capital. That could be a city you see on top of Rainer’s head ( is that a Hauptstadt?) and for any subject that represents a country. Now you can see the bullring in the city where the Kiefer is running at a torero. The torero doesn’t have to be Rainer.
I also would create the SEA-IT images independently for the country and city so they can be reused. Add details to Ukraine that are appropriate to the Ukraine. That way when you go to Spain and want to create a bull fight scene, it won’t be a problem. I enjoy food. If Rainer is wearing his UK chef’s uniform, he might be stabbing a crispy brown Chicken Kiev and getting hot butter on it in his coastal restaurant.
As Kiev is the “mother of Rus cities,” I might use a mother wearing a headscarf putting in a shiny new pair of dentures in her large Kiefer in her simple bathroom. Now an association can be asked “why did the mother go to Rainer’s restaurant in the coastal city?” A: To eat a Chicken Kiev with her new dentures.
The association query that is the one for the captial of Ukraine would be “what is going on in the city on top of Rainer’s head?” A: a woman in a headscarf is eating Chicken Kiev with her dentures getting stuck in them while the butter runs down on Rainer’s chef’s uniform.
Detail is important for each visual image to help recall. But it has to be relevant to that image and then the image is much more easily associated with another image by a replacement or addition of an action in most cases or other types of interactions. This is where a scene type of association would be created when you merge the two to exist in the same location. To get the fullest visual sentence for each image, fill out the SEA-IT data for each country and city and then make an association.
This is just my system for creating memorable narratives and I hope you find it helpful. The data types and other terms aren’t used by other people yet as far as I know.