The convolution layer is the most important part of a CNN as it is the layer that learns the image features. Before we dive deep into convolutions, we will learn about image features. Image features are the part of the image that we are most interested in.
Some examples of image features are as follows:
- Edges
- Colors
- Patterns/shapes
Before CNNs, the extraction of features from an image was a tedious process—the feature engineering done for one set of images would not be appropriate for another set of images.
Now, we will see what exactly a convolution is. In simple terms, convolution is a mathematical term to describe the process of combining two functions to produce a third function. The third function, or the output, is called the feature map. Convolution is the action of using a kernel or filter applied to an input image, and the output is the feature map.
The convolution feature is executed by sliding a kernel over the input...
