<p>While lower-order convolution kernels are usually in smaller size comparing to the input image,
extracted features focus more on local perception. However, higher-order convolutions enable
expansions in the overall receptive field which gradually converts local features to global features
which is also the case how human gazes at an object and recognizes it. 
Here you go my friend
<a href="https://iopscience.iop.org/article/10.1088/1742-6596/1087/6/062032/pdf" rel="nofollow noreferrer">https://iopscience.iop.org/article/10.1088/1742-6596/1087/6/062032/pdf</a></p>

<p>and this one
<a href="https://arxiv.org/pdf/1904.04447.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1904.04447.pdf</a></p>

<p>s. If we only use the neighbor patterns
extracted by CNN, many useful global feature interactions will be
lost. This is also why CNN models do not perform well for CTR
prediction task. To overcome this limitation, we perform CNN and
MLP, which complement each other, to learn global-local feature
interactions for feature generation</p>
What is the type of extracted CNN features: global or local feature type or both of them
<p>As it is known that local features describe the local structure of the image contents while the global features describe the image contents as a whole. Convolution neural network which is under deep learning field makes to extract important feature by itself, I want to understand what type of the extracted feature by CNN is they are local or global feature or both of them? and why? is there anyone can help me in some analysis or references answering my question. Thanks.</p>