Temporal Consistency Measurement for Video Segmentation

The patent introduces an intermediary measurement approach that uses feature extraction and similarity computation as intermediate steps between raw segmentation masks and temporal consistency evaluation. By extracting features from segmentation masks and comparing them across frames using similarity measurements, the system creates a more reliable and precise temporal consistency metric that overcomes the limitations of direct mask comparison methods.

The patent replaces the direct mechanical comparison of segmentation masks with a feature-based similarity measurement system. Instead of directly comparing mask pixels, the system extracts features from masks and computes similarity metrics, substituting the simple but unreliable direct comparison mechanism with a more sophisticated feature-based approach that handles occlusion and large movements reliably.

The patent extracts essential features from segmentation masks to create a simplified representation that retains the key information needed for temporal consistency evaluation. By taking out only the necessary features rather than processing complete masks, the system achieves high measurement precision while reducing computational power requirements compared to full mask comparison methods.