Neural Network Confidence Scoring for Selective Pixel Reclassification
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Solution Overview
Problem
Existing systems face issues with misclassification of objects in images due to low confidence in classification results, leading to errors that affect decision-making processes.
Innovation Solution
A system that assigns confidence values to pixel-wise classifications using neural networks, incorporating score distribution transformation, normalization, and determination processes to enhance the reliability of image segmentation and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks provide classification results with low confidence, then classification speed is maintained, but classification accuracy deteriorates leading to errors
Solution Approach 1:
The system performs partial reprocessing by applying multiple neural networks only to pixels with low confidence values rather than reprocessing the entire image. This selective approach improves classification accuracy for uncertain pixels while minimizing additional processing time.
Solution Approach 2:
The system uses confidence values as feedback to determine which pixels require further processing. Pixels with confidence values below a threshold are identified and reprocessed, creating a feedback loop that improves accuracy only where needed rather than uniformly processing all pixels.
2Reliability
If multiple neural networks are used to improve classification reliability, then classification accuracy improves, but computational complexity increases
Solution Approach 1:
The image processing task is segmented into two stages: initial classification using a first neural network, and selective reclassification using a second neural network only for low-confidence pixels. This segmentation reduces overall computational complexity compared to applying multiple networks to all pixels.
Solution Approach 2:
The system applies different processing quality to different regions of the image based on confidence values. High-confidence pixels receive standard processing while low-confidence pixels receive enhanced processing with additional neural networks, optimizing the balance between reliability and computational complexity.
3Measurement precision
If confidence values are assigned to all pixels, then decision-making accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the confidence values from the neural network output, separating this critical information from the full classification data. This extraction allows decision-making systems to use confidence information without processing the complete dataset, reducing data volume requirements.
Solution Approach 2:
Confidence values serve as an intermediary between the neural network classification and the decision-making system. Rather than transmitting full classification results, the confidence values mediate by providing a compact summary that enables efficient decision-making about which classifications to trust.
Data Source
AI summary
Apparatuses, systems, and techniques to generate one or more confidence values associated with one or more objects identified by one or more neural networks. In at least one embodiment, one or more confidence values associated with one or more objects identified by one or more neural networks are generated based on, for example, one or more neural network outputs.