Image Recognition Output Distribution Filtering
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Solution Overview
Problem
Current image recognition systems often fail to accurately disambiguate image data into objects or features due to inconsistencies in confidence values, which can lead to incorrect identification of the highest confidence feature or object.
Innovation Solution
The method involves generating an image classification output distribution for a plurality of image features based on analysis and training data, applying filters such as optical character recognition, classroom syllabus data, learner model topics, and user-teacher communications to refine the output until a highest confidence value meets a threshold, thereby identifying the most accurate feature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image recognition systems directly select the highest confidence feature from the output distribution, then the identification process is simple and fast, but the accuracy is reduced due to inconsistencies in confidence values
Solution Approach 1:
The patent introduces multiple intermediary filters (syllabus filter, learner model filter, communication filter) that act as mediators between the raw image recognition output and the final feature selection. These filters process the output distribution sequentially, refining confidence values at each stage to eliminate inconsistent or incorrect high-confidence predictions, thereby improving accuracy without requiring fundamental changes to the core recognition system
Solution Approach 2:
The system performs preliminary actions by applying multiple filtering stages before final feature selection. Each filter pre-processes the output distribution to adjust confidence values, ensuring that by the time the highest confidence feature is selected, the confidence values have already been refined and corrected for known inconsistencies, leading to more accurate identification
2Measurement precision
If multiple filters are applied to refine the output distribution, then the accuracy of feature identification is improved, but the processing time and computational complexity increase
Solution Approach 1:
The patent segments the filtering process into distinct, modular filters (syllabus filter, learner model filter, communication filter) that process the output distribution in sequence. Each filter handles a specific aspect of refinement independently, allowing for efficient processing and enabling the system to stop early if confidence values meet thresholds, thus managing processing time while maintaining accuracy
3Reliability
If confidence values are adjusted through multiple filtering stages, then the reliability of the selected feature is improved, but the system complexity increases
Solution Approach 1:
Multiple intermediary filters are introduced as specialized components that each handle specific aspects of confidence value refinement. The syllabus filter incorporates domain knowledge, the learner model filter adapts to individual user needs, and the communication filter integrates contextual information. This modular intermediary approach improves reliability by addressing different sources of inconsistency separately, while keeping each filter's complexity manageable and well-defined
Data Source
AI summary
Data representing an image is received by an image recognition system. An image recognition system generates an image classification output distribution for a plurality of image features based on analysis of the data representing the image and training data stored for the image recognition system. One or more filters are applied to the image classification output distribution to obtain an updated image classification output distribution. A highest confidence value is selected from the updated image classification output distribution. A selected image feature associated with the highest confidence value is identified from the plurality of image features. Information associated with the selected image feature is obtained from a database and communicated to the user's device by the image recognition system.