I am learning this book "Deep Learning and Convolutional Neural Networks for Medical Image Computing"
On the other hand, CNN models have been proved to have much higher modeling capacity, compared to the previous image recognition mainstream pipelines, e.g., HAAR, SIFT, HOG image features followed by spatial feature encoding,
thenrandom forest or support vector classifiers. Given millions of parameters to fit during model training (much more than previous pipelines), CNN representation empowers and enables computerized image recognition models, with a good possibility to be able to handle more challenging imaging problems. The primary risk is overfitting since model capacity is generally high in deep learning but often very limited datasets are available (that are with good quality of labels to facilitate supervised training). The core topics of this book are represented by examples on how to address this task-critical overfitting issue with deep learning model selection, dataset resampling and balancing, and the proper quantitative evaluation protocols or setups.
then indicates that the author put "CNN models" (method_1), "the previous image recognition mainstream pipelines" (method_2) and "spatial feature encoding" (method_3) in the comparison.
the question is
in the modeling capacity perspective, does
then indicate method_1 > method_2 > method_3 ?