Can a single image be a positive example for multi

Question

Bouquets of flowers are a fairly accurate analogy for our problem domain, and we have another S.O. question out there asking about the feasibility of a different approach to our problem/goal.

What if, rather than making classes by flower types, we made our classes according to the actions we need to take depending on the contents and complex combinations of the bouquet?

Let's say that, if in the bouquet in our test image, there are:
>9 roses, >14 pansies, <1 marigold, any qty of other flowers
then we need to take, both, action-a & action-d.

So, then, the same image would be used as a positive example for both class action-a and class action-d.
Inversely, there would absolutely be positive action-d examples which would be negative action-a examples, and vice versa.

Of course, even with this simplification it still gets quite complex.
I imagine this approach would need a huge number of training images.
Even still, I'm hopeful that it might work.

Thoughts?

Answer 1

I think you are on right path. but u have to make sure that u have enough no of images for training and no. of flowers in each image should be clearly visible. Try it

Answer 2

Yes, you can have the same image in >1 classes inside 1 classifier, as long as you have >=10 unique images per class AND >=20 total unique images in the classifier in total, including any negative_examples.

However, you should be careful about what you are "teaching" the system by doing this.

Classes within a classifier are meant to be mutually exclusive. Internally the system is trying to figure out what makes the positive examples of one class different from all the other examples in a classifier's training data.

If the system discovers an exact duplicate of an image file in more than one class of a single classifier , it will use it as a positive example of both classes. Exact duplicates are determined by the check sum of the image file.