Portrait Segmentation Using Skip Fusion and Hybrid Loss

The patent replaces traditional color-based segmentation methods with a neural network-based deep learning approach. The neural network learns complex patterns and features from training data, enabling accurate segmentation regardless of color similarity between foreground and background. This substitution of mechanical color comparison with intelligent pattern recognition resolves the contradiction between implementation simplicity and segmentation accuracy.

The patent extracts and removes the dependency on green screen equipment and controlled environments from the segmentation process. By using a neural network trained on diverse portrait images, the system achieves accurate segmentation without requiring specific background colors or specialized equipment. This extraction of the segmentation capability from environmental dependencies resolves the contradiction between accuracy and device complexity.

The patent employs an encoder-decoder architecture with skip fusion connections that segment and process features at multiple levels. The encoder extracts features at different hierarchical levels, and the decoder reconstructs the segmentation mask by fusing these features through skip connections. This multi-level feature fusion approach improves both the adaptability for handling various portrait configurations and the precision of the final segmentation mask and center alignment.

The patent uses skip fusion connections to selectively fuse features at different hierarchical levels, focusing computational resources on the most informative feature combinations. Rather than processing all possible feature interactions through numerous hidden layers, the skip fusion mechanism strategically combines features that provide the most value for segmentation accuracy. This partial action approach maintains high accuracy while reducing unnecessary computational overhead.