How do CNNs perform hierarchical abstraction from pixels to semantic concepts?

CNNs progressively transform raw pixels into edges, textures, parts, and finally semantic object representations through stacked convolution layers.

CNNs build hierarchical abstraction by stacking convolutional layers with nonlinear activations. Early layers detect simple patterns like edges and gradients. Intermediate layers combine these into textures and object parts. Deep layers integrate these parts into full semantic concepts such as faces or vehicles. This hierarchy emerges due to local receptive fields, weight sharing, and backpropagation-driven feature optimization.