Bayesian Image Tagging with Multiple AI Systems
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Solution Overview
Problem
Current methods for feature detection in scanned images, such as photos, are labor-intensive and unreliable due to human error and variability among AI systems, leading to inconsistent tagging and retrieval challenges.
Innovation Solution
The use of multiple AI systems with Bayesian inference techniques to process and combine confidence scores for feature detection, along with human confirmation and automated synonym lookup, to enhance the reliability and accuracy of feature detection and tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple AI systems are used for feature detection, then reliability of feature detection is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple independent AI systems into a unified feature detection framework where each AI system processes the same scanned image and their results are merged through Bayesian inference. The confidence scores from multiple AI systems are combined using probabilistic methods to produce a more reliable overall detection result, resolving the contradiction by achieving higher reliability through systematic integration rather than ad hoc approaches
Solution Approach 2:
The patent introduces Bayesian inference as an intermediary mechanism that mediates between the outputs of multiple AI systems and the final feature detection result. This intermediary layer processes the confidence scores from different AI systems, applies probabilistic reasoning, and produces a consolidated detection outcome, thereby managing the complexity of integrating multiple AI systems while maintaining high reliability
2Measurement precision
If Bayesian inference techniques are used to combine confidence scores, then accuracy of feature detection is improved, but computational requirements increase
Solution Approach 1:
The patent applies partial Bayesian inference by focusing computational resources on combining confidence scores only for features that are detected by at least one AI system with sufficient confidence. Rather than performing exhaustive Bayesian calculations for all possible features, the system selectively applies the inference technique where it provides the most value, thereby improving accuracy for critical detections while limiting excessive computational energy consumption
3Adaptability or versatility
If automated synonym lookup is implemented, then vocabulary limitations are addressed, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing synonym relationships in a lookup table or database structure before the actual tagging process. When a feature is detected, the system quickly retrieves pre-prepared synonym information rather than performing complex linguistic analysis in real-time. This approach expands vocabulary coverage adaptively while minimizing the time penalty during active processing
Data Source
AI summary
Methods and apparatus for using one or more feature recognition system(s) to identify features in a scanned image and to associate tags with identified features are described. Probabilities of a feature being present are taken into consideration in some embodiments. In various embodiments once a tag has been determined as corresponding to a feature which has been confirmed as being in the scanned image synonyms for the tag word are identified and also associated with the scanned image. By using results of multiple automated feature recognition systems and generating overall probabilities that a feature is present in an image more reliable tagging can be implemented in an automated manner than in system which rely on a single AI system without the need for extensive operator input with respect to identification of all features in the image which are to be tagged.