I am working on a multi-class classification problem using images. I have a training library of images containing 9 different classes of object, however I will also need to train my image classifier to detect negative image examples (i.e. the image contains none of the 9 objects classes in the training library).
Assuming image frequencies in my training library are perfectly balanced, and each class contains n images, what is the best number of negative examples to include in the dataset? Intuition tells me to create n examples for the 'negative' class. Could anyone comment on the appropriateness of this?
Also if anyone could point towards any academic work published on this topic, I would be very grateful.