Species Recognition Feedback Loop for Confidence Thresholds
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Solution Overview
Problem
Current visual recognition systems face challenges in accurately identifying species and objects within photographs, often resulting in low confidence levels and incorrect classifications, necessitating improved methods to enhance identification accuracy.
Innovation Solution
A computer system and method that utilizes predefined markers with associated properties and measured values to identify objects, employing feedback loops to gather additional information when initial confidence levels are below thresholds, comparing these markers with classifiers in a database to determine species and object confidence levels, and iteratively refining the identification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If visual recognition systems compare photographs with database pictures to identify objects, then identification speed is improved, but identification accuracy deteriorates due to low confidence levels and incorrect classifications
Solution Approach 1:
The system implements feedback loops that continuously refine identification results. When initial confidence levels are below thresholds, the system requests additional photographs and recalculates confidence levels, iteratively improving identification accuracy while maintaining efficient processing through automated feedback mechanisms.
Solution Approach 2:
The system performs preliminary confidence level assessments before final identification. By evaluating confidence levels upfront and determining whether additional analysis is needed, the system prepares identification results in advance, ensuring accuracy without sacrificing speed for cases that meet confidence thresholds.
2Measurement precision
If the system requests additional photographs to improve confidence levels, then identification accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial action by requesting only the necessary number of additional photographs based on initial confidence levels. Rather than always requesting maximum additional images, the system adapts the number of requests to achieve sufficient confidence, reducing unnecessary processing time while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts processing requirements based on initial assessment results. Confidence level thresholds and additional photograph requirements are flexible rather than fixed, allowing the system to optimize processing time by adapting to each specific identification case's needs.
3Reliability
If the system uses multiple markers and feedback loops for identification, then reliability of identification is improved, but system complexity increases
Solution Approach 1:
The system uses universal marker detection and confidence level calculation mechanisms that work across different object types and species. The same feedback loop infrastructure serves multiple identification functions, reducing the need for separate complex systems for each identification task while maintaining high reliability.
Solution Approach 2:
The system performs self-assessment of confidence levels and automatically determines when additional information is needed without external intervention. This self-service capability reduces the need for complex external validation systems, improving reliability through automated consistency checks while managing system complexity.
Data Source
AI summary
The present invention provides a method and system for identifying a species or object having identifying property markers by comparing the markers with values stored in a database, then comparing a group of species/objects which meet a predetermined threshold level to select a species/object having a highest confidence level. If the species/object having the corresponding highest species confidence level does not meet a predetermined species/object confidence level threshold value stored in the database, then a feedback loop provides for gathering additional information to accurately identify the species by repeating the above-identified steps in consideration of the additional information until a species/object meets or exceeds the predetermined species confidence level threshold value.