Image classification: Strategies for minimal input count
After running a few tests and collecting some training data I got a rough impression of how much data is needed. The question is still not answered entirely but I figured these insights might help others:

So how many images did I need?

Architecture: 
- Pre-trained Inception V3 model with Imagenet data
- Removed last layer 

Data / Images per category:


101 images  
258 images
45 images
1064 images
259 images
607 images
161 images


Complexity:
Mixed complex sceneries with simple images. 

Result:
The quality depends a lot on how difficult the task is. In my scenario the images to be classified where rather similar to the ones in the training data. In these situations all categories with 160 images and above are recognised rather reliably.

I will do some more testing and update my answer.