Feature Map
What does Feature Map mean in CNN computer vision?
In Convolutional Neural Networks (CNNs) used in computer vision, a feature map, also known as a convolutional feature map or activation map, is a two-dimensional array or grid of numbers resulting from the application of convolutional filters (also called kernels) to an input image or a previous layer’s feature map. Feature maps play a crucial role in the CNN architecture and are responsible for detecting different visual features or patterns in the input data.
Here are the key points about feature maps in CNN computer vision:
Convolution Operation: In CNNs, convolutional layers are used to detect and extract various features from the input data. These layers slide small filters (kernels) across the input image or the previous layer’s feature map. At each location, the filter computes a dot product between itself and the corresponding region of the input. This operation results in a single value in the output, and these values collectively form the feature map.
Number of Feature Maps: In a convolutional layer, multiple filters are applied in parallel. Each filter detects a different feature or pattern, such as edges, textures, or higher-level features like shapes or object parts. The output of each filter corresponds to a separate feature map.
Feature Visualization: Feature maps represent the presence or absence of specific features at different spatial locations in the input data. High values in a feature map indicate the presence of the detected feature, while low values indicate its absence.
Hierarchical Feature Extraction: In a CNN, as you progress through the layers, the feature maps become increasingly complex and abstract. Lower layers may detect basic features like edges and corners, while higher layers learn to recognize more complex patterns, such as object parts or entire objects.
Spatial Dimensions: Each feature map has its spatial dimensions, and these dimensions depend on the size of the input, the filter size, and the stride used during convolution. As you go deeper into the network, the spatial dimensions often decrease, reflecting a more abstract and compact representation of the input.
Feature maps play a central role in enabling CNNs to automatically learn and extract meaningful features from raw image data. These features are critical for various computer vision tasks, such as image classification, object detection, image segmentation, and more, as they capture the hierarchical and abstract information necessary for these tasks.
