Yes, I believe, its trying to explain how DL recognize something complicated all at once, the model can progressively build its understanding from simpler features. It seems similar to how humans might recognize an object by first noticing shapes, edges, and individual parts.

I also found this interesting because it shows that even a good ML algorithm can perform poorly if the data is not represented in a useful way. Thus, deciding what information the model receives is an important part of ML.