Feature extraction

Chances are, that raw pixel values are not the most informative way to represent the data, as we have already realized in Chapter 3, Finding Objects via Feature Matching and Perspective Transforms. Instead, we need to derive a measurable property of the data that is more informative for classification.

However, often it is not clear which features would perform best. Instead, it is often necessary to experiment with different features that the modeler finds appropriate. After all, the choice of features might strongly depend on the specific dataset to be analyzed or the specific classification task to be performed. For example, if you have to distinguish between a stop sign and a warning sign, then the most telling feature might be the shape of the sign or the color scheme. However, if you have to distinguish between two warning signs, then color and shape will not help you at all, and you will be required to come up with more sophisticated features.

In order to demonstrate...