How do I reduce a scanned image to a consistent hash?

Question

I'd like to be able to scan an image and reduce it to a consistent hash that I can subsequently compare to a new scan to see if the two images are the same.

Any help in this regard would be greatly appreciated!

Answer 1

The following approaches are possibly more powerful than what you actually need.

In computer vision, an active area of research is in recognition.

For instance, if I were to build a cleaning robot for my house, it should be able to recognize my dog (so as to not spray lethal chemicals on it). This is made more difficult since the robot won't necessarily look at the dog from the same perspective every time (and it can move). ie it should recognize it is my dog from the sides, the front, or the back.

To train this robot, I show it a few pictures of my dog under different lighting conditions, and it should be able to recognize it in the future.

Different approaches are in use to extract the salient features from an image which can help you recognize the same features even if the picture was taken in different lighting or from a different angle.

Some feature extraction techniques include the following:

• SIFT (Scale-Invariant Feature Transform)

• GIST (GIST descriptor)

• HoG (Histogram of Oriented Gradients)

• Shape Context

• Texton

• Spin-Images

However, rather than manually extracting features, many modern systems use a neural-network machine learning method so the robot/computer can learn to recognize objects, using perhaps the same way humans learn.

I've never done image recognition, so I am not sure about their advantages/disadvantages, but I found the subject fascinating, and I hope that computers will get better at recognizing stuff (vision, voice, gesture, etc).