Again: convolutional filters existed long before CNNs did.
Yet no one solved ImageNet with some convolutions and hand-engineered features. You can't get that performance even if you shift a set of hand-engineered feature across an image in a convolutional fashion. No one solved it with any previous approach. (Are we to believe that CNNs are the very first method to ever try to exploit some spatial structure...?)
So deep networks do bring things to the table beyond hand-engineered features and you are simply wrong.
Yet no one solved ImageNet with some convolutions and hand-engineered features. You can't get that performance even if you shift a set of hand-engineered feature across an image in a convolutional fashion. No one solved it with any previous approach. (Are we to believe that CNNs are the very first method to ever try to exploit some spatial structure...?)
So deep networks do bring things to the table beyond hand-engineered features and you are simply wrong.