Hi everybody I've been struggling around with a question about probabilities. I have a background in physics, so I though I was able to handle it but after some days thinking I just give up. Here it is:
I want to classify a large group of directories (thousands). Each of them contain hundreds of images. The images can be for example: cats, dogs, birds, cars, etc. Some directories can contain mostly images of one type, others a mix of them.
I have an algorithm that takes one image and prints out the probabilities that this image belong to a pre-trained set of images (Random Forest). eg. 0.8 * cat, 0.1 * dog, 0.1 * bird, 0.0 * others... Because I cannot train all possible images in the directories, I also have a 'others' type of images ('0' class).
My task is to classify the directories by estimating the probability(ies) that a chosen directory contains images of a given type(s) without to check all images inside, but for example picking up randomly a certain number $n$ of images of a total of $N$ and use their individual result from the algorithm.
Is this a good idea for classifying all my directories? Statistically speaking how can I formulate my problem. Any hint or idea is welcome.
Thanks in advance!