Hi I have a programming problem that I would like to solve using some artificial intelligence technique. I really dont know where to start. I would like some guidance as to what methodology to pursue.
Lets say I have 10,000 images of random people, and I need to detect elderly people in images. I might have algorithms like wrinkle detector, glasses detector, walking cane detector, missing teeth detector, skateboard detector, Playstation detector, etc. Each algorithm does a scan independently and outputs a number from 0 to 10 on the likelihood it thinks the image contains that item. Lets assume that works. There might be 100 different algorithms.
My set of 10,000 images would be divided by a human into two groups, those that contain an elderly person, and those that do not.
Now I need to develop a system that takes the series of values from the algorithm modules, when given an image to analyze, and calculates a single value that represents the likelihood that an image has elderly people in it or not.
During training I would like it to be able to automatically build rules by analyzing all the algorithms' outputs. For example:
If wrinkle detector, glasses detector, walking cane detector and missing teeth detector all output a high number, then output a high number.
If wrinkle, glasses, cane and teeth detectors are high, but playstation and skateboard detectors are also high, then output is neither low nor high.
hands detector and clothes detector should be essentially ignored as old and young people both have those (hopefully)
What type of technology should I be implementing for the automated rule building system? Is this better solved by a neural network system? A fuzzy logic system? Something else?
Thanks for any advice.