[pedrosorio]

Machine learning was extensively used in image processing before 2013 / deep learning.

The main difference is that you’d write code to extract features from the image and then learn a model using those features (as opposed to using the pixel data directly and learning a model from that as in CNNs).

As an example, you wouldn’t necessarily write code for “fur texture” but instead would extract histograms of pixel brightness gradients and feed those (along with other things) to a machine learning algorithm. In this example, fur texture would generate a different histogram (to be used as a feature) than skin texture.

https://en.m.wikipedia.org/wiki/Histogram_of_oriented_gradie...

[twa927]

Ok, so this depends on what algorithms are used for the feature detection ("edges in an image, and an eye detector, and a texture analyser for fur"). I'm guessing hand-coding an algorithm for detecting edges in an image can be done successfully, but it looks much harder for "an eye detector", so it needs "machine learning"

What I meant when asking for a name of an algorithm class are algorithms where the feature extraction is done using hand-coded algorithms.

[layoutIfNeeded]

You can call them “handcrafted decision trees” then.