I have read about SVM and understood that for complex divisions, the SVM theoretically plots the data into a higher dimensional plane such that the in the new dimension the data is linearly separable and to achieve this in a practical way, it uses kernel functions which in place of actually transforming the data into a higher plane gives the boundary distance between the boundary and that data point.
But how does this work in the case of an image classifier? Let's say we need to classify pictures as either dog or cat. In this scenario, a CNN model would learn features like ear size, face shape, nose shape, and other visual characteristics of the training set to classify between a dog and a cat. But what does SVM learn while training and how does it work in this case?