Locally connected pixels are highly correlated in images. In machine learning, using SVM or other classifiers we would want our features to be as unrelated as possible.
However, in CNNs why do we want exactly the opposite thing? We are focusing on highly correlated features (i.e. pixels) to convolve with our weight vector (say a 5x5 filter)?
I understand its usage to reduce the no. of parameters, but I do not clearly understand that why local connections should work? Basically what is the motivation behind considering local connections in images?