How Are Convolutional Operations Calculated?
Convolutional operations are fundamental processes in the field of computer vision, particularly in deep learning. They involve sliding a small matrix, known as a kernel or filter, across an input image to extract features. This process is crucial for tasks such as image classification and object detection.
1. The Convolution Process
The convolution operation begins by selecting a kernel, typically a square matrix (e.g., 3x3 or 5x5). This kernel contains weights that help in feature extraction. The kernel is applied to the input image by placing it on the top-left corner and performing element-wise multiplication between the kernel and the overlapping pixels of the image.
2. Summation and Sliding
After the element-wise multiplication, the results are summed to produce a single output pixel in a new feature map. The kernel is then slid across the image in a predefined stride (e.g., moving 1 pixel at a time) until the entire image is processed. This form of operation enables the network to learn spatial hierarchies of features.
3. Activation Function
The output feature map often undergoes a non-linear activation function (like ReLU) to introduce non-linearity into the model, allowing it to learn complex patterns.
By stacking multiple convolutional layers, networks can learn increasingly abstract features, crucial for advanced computer vision tasks in deep learning.