Pooling

After obtaining features using convolution, the next step is to use them in for classification. In theory, one could use all the extracted features with a classifier (e.g., softmax regression) but this can be computationally challenging. Consider for instance images of size 96x96 pixels and 400 features that are 8x8 each and convolved over the entire image; each features after (valid) convolution results in <math>(96-8+1)*(96-8+1)=7921</math> and since we have 400 features, this results in a feature vector of <math>(98^2) * 400 = 3,168,400</math> features per example. Learning a classifier with inputs having 3+ million features can be unwieldy and also prone to over-fitting.

After obtaining features using convolution, the next step is to use them in for classification. In theory, one could use all the extracted features with a classifier (e.g., softmax regression) but this can be computationally challenging. Consider for instance images of size 96x96 pixels and 400 features that are 8x8 each and convolved over the entire image; each features after (valid) convolution results in <math>(96-8+1)*(96-8+1)=7921</math> and since we have 400 features, this results in a feature vector of <math>(98^2) * 400 = 3,168,400</math> features per example. Learning a classifier with inputs having 3+ million features can be unwieldy and also prone to over-fitting.