Image Recognition Confidence Threshold and Third-Party Labeling
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Solution Overview
Problem
Existing image recognition technologies consume significant resources and suffer from reduced accuracy due to errors in recognition models, making them unsuitable for personal users and requiring adjustments based on machine output.
Innovation Solution
A method and apparatus that acquire and send images to a server for recognition, using a confidence parameter to determine identification information as a recognition result when above a threshold, or retrieving labeled information from a third-party platform when below the threshold, combining automatic recognition with third-party labeled data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image recognition is performed using a recognition model established by analyzing features of massive images, then recognition capability is provided, but resource consumption increases and recognition accuracy decreases due to model errors
Solution Approach 1:
The system segments the recognition process into two parts: server-side model-based recognition and client-side threshold-based decision making. The server performs computationally intensive image analysis using recognition models, while the client device only needs to compare confidence parameters against thresholds and select from predefined options, significantly reducing local resource consumption while maintaining recognition accuracy.
Solution Approach 2:
The server acts as an intermediary between the image data and the client device. It processes the image through recognition models, generates confidence parameters, and sends processed results to the client. This intermediary approach allows complex processing to occur remotely while the client performs only simple comparisons, reducing both resource consumption and accuracy loss.
2Reliability
If recognition model adjustment is based solely on machine output, then automation is maintained, but recognition accuracy decreases due to deviation caused by errors
Solution Approach 1:
The system implements a feedback mechanism where confidence parameters from server recognition are continuously monitored and compared against thresholds. When confidence falls below the threshold, the system automatically triggers manual intervention to collect correct labels, which are then used to retrain and improve the recognition model. This closed-loop feedback ensures both automation maintenance and accuracy improvement.
Solution Approach 2:
The system enables self-service by automatically handling the entire workflow: performing recognition, evaluating confidence, detecting low-confidence cases, collecting manual corrections, and retraining models without requiring constant human oversight. This automated self-service approach maintains high automation levels while improving accuracy through selective manual intervention.
3Productivity
If confidence parameter comparison with threshold is used, then quick decision making is achieved, but recognition accuracy is lost when confidence is low
Solution Approach 1:
The system dynamically adjusts its decision-making process based on confidence levels. For high-confidence predictions above the threshold, it makes quick automatic decisions to maximize speed. For low-confidence predictions below the threshold, it triggers manual review to ensure accuracy. This dynamic adaptation allows the system to optimize for speed when appropriate and for accuracy when needed.
Solution Approach 2:
The system changes the operational parameter from purely automated model-based recognition to a hybrid approach that incorporates confidence threshold evaluation. By monitoring the confidence parameter and changing the recognition strategy based on its value (automatic acceptance above threshold, manual review below threshold), the system achieves both fast decision-making for clear cases and accurate results for ambiguous cases.
Data Source
AI summary
The present disclosure discloses a method and an apparatus for recognizing an image. A specific implementation of the method comprises: acquiring a to-be-recognized image containing a to-be-recognized object; sending the to-be-recognized image to a server, and receiving identification information of a target object corresponding to the to-be-recognized object returned by the server, obtained by recognizing the to-be-recognized image, and a confidence parameter returned by the server; and determining the identification information of the target object as a recognition result when the confidence parameter is greater than a confidence threshold; or acquiring labeled information associated with the to-be-recognized image from a third-party platform and determining the labeled information as the recognition result when the confidence parameter is smaller than the confidence threshold.