Convolutional Neural Networks (CNNs) have revolutionized image classification in the field of computer vision. CNNs are a type of deep learning algorithm that excel at analyzing images and extracting relevant features for accurate classification.

The key idea behind CNNs is to apply filters to input images to identify specific features, such as edges, corners, or textures. These filters, also known as convolutional layers, slide over the input image, performing element-wise multiplications and aggregations to create feature maps. Through multiple layers of convolutions, pooling, and non-linear activation functions, CNNs can learn complex hierarchical representations of objects in images.

For example, let's say we want to train a CNN to classify images of cats and dogs. We can start by feeding the CNN with labeled images of cats and dogs, allowing it to learn the patterns and features that differentiate the two classes. By optimizing the network's weights using techniques like gradient descent, the CNN can gradually improve its ability to distinguish cats from dogs. This trained CNN can then classify new, unseen images with high accuracy.