I was under the impression the training data given to train an SVM consisted of image features, but after reading this post again, the training_mat that is given to the SVM in the example is just the img_mat flattened to 1-Dimension.
So my question is, when training an SVM, do you give it whole images in their entirety, row by row, or do you detect and extract the features, and then flatten a Mat of that into 1-Dimension?
You can extract features, or you can use pixel intensity values as the features. In this example, they have done the latter. In this case, you end up with a very high number of features that many of them may be not useful. This makes the convergence of the SVM training more difficult, but can be still possible. Based on my personal experience, SVM works better if you extract a lower number of "good" features that best describe your data. However, in recent years, it has been shown that state-of-the-art estimators like deep neural networks (when used instead of SVM) can perform very well with only using the pixel intensity values as features. This has eliminated the need for feature extraction in the methods that has led to state-of-the-art results on public data sets (like ImageNet)
