Image Density Prediction Model Segmentation

The patent segments the use environments into multiple categories and creates separate prediction models for each category. Instead of using a single average model, the system divides the broad application domain into specific segments (different use environments) and develops specialized models for each segment, thereby achieving both broad coverage and high accuracy within each segment.

The patent implements a dynamic model selection mechanism that adapts to different use environments in real-time. The system dynamically selects or adjusts the appropriate prediction model based on the current use environment, allowing the model to transition from a static average model to a dynamic environment-specific model, thus improving prediction accuracy without sacrificing versatility.

The patent applies local quality by selecting training data based on its relevance to specific use environments. Instead of uniformly using all data, the system evaluates the quality and appropriateness of each data point for the target use environment, ensuring that only suitable data is used for correction. This selective approach maintains high prediction accuracy by preventing inappropriate data from degrading the model.

The patent implements a feedback mechanism where the system evaluates the impact of correction data on prediction accuracy. By monitoring whether the correction improves or degrades performance, the system can adjust its data selection criteria and exclude inappropriate data from future corrections, thereby maintaining high prediction accuracy while utilizing sufficient training data.