Bouquets of flowers are a fairly accurate analogy for our problem domain.
For an example, let's assume a test image of thirty flowers:
- Roses: 10
- Poppies: 9
- Daisies: 5
- Lillies: 5
- Sunflowers: 1
Is there a training approach that might get Watson to look at pictures of bouquets and be able to reply with a density of a given flower type, or even a ratio or something?
If there are any ideas, should we train with images of single/isolated or multiple/grouped of each type of flower?
...or a combination of both?
ANY ideas/suggestions would be welcome!!!
EDIT:
Alternatively, rather than making classes by flower-type, we could class by action-needed ??
But, maybe that's a different enough idead to be it's own question.
In part, it depends on how much control you have over the images that you need to classify, and the granularity of the classification that you need to make. If, for example you're guaranteed to always have a top down view of the bouquet that shows all the different flowers clearly and other extraneous objects are generally not in the scene, then you probably could train a classifier for something like five density levels for each flower type. For example, the Daisy classifier would have five classes: 0 to 20% daisies, 20 to 40% daisies, 40 to 60% daisies, 60 to 80% and over 80% daisies.