Classification Model Update via User Correction Feedback
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Solution Overview
Problem
Existing pattern recognition systems face challenges in improving the performance of recognition models, particularly in accurately categorizing input patterns and updating classification models based on user corrections, which affects classification accuracy.
Innovation Solution
An image processing device with a determination unit for categorizing input image data, a display unit for user interaction, and an updating unit that allows users to correct categories and update the classification model accordingly, using features like Haar Wavelet transforms and projection distance methods for improved classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a classification model is used to automatically categorize input image data, then processing speed and productivity are improved, but classification accuracy and reliability deteriorate due to model limitations
Solution Approach 1:
The system implements a feedback mechanism where user corrections to classification results are fed back to update and refine the classification model. The determination unit displays category determination results to the display unit, and when users provide corrections through the acceptance unit, the updating unit uses these corrections to iteratively improve the model's accuracy, resolving the contradiction between automated processing speed and classification reliability
Solution Approach 2:
The classification model performs self-improvement by automatically updating itself based on user feedback. The updating unit retrains the classification model using corrected data from users, enabling the system to autonomously enhance its own classification accuracy without requiring complete manual reclassification, thus maintaining productivity while improving reliability
2Measurement precision
If user corrections are accepted to improve classification accuracy, then recognition precision is improved, but device complexity and operational complexity increase
Solution Approach 1:
The display unit serves multiple functions: it displays both the original image data and the determination results, and also serves as the interface for users to provide corrections. The acceptance unit accepts both initial classification inputs and correction inputs through the same interface, reducing system complexity while enabling precision improvement through user feedback
3Measurement precision
If the classification model is updated frequently with user corrections, then classification accuracy is improved, but processing time and operational time increase
Solution Approach 1:
The system performs preliminary classification using the existing model to quickly categorize image data before presenting uncertain or low-confidence cases to users for correction. This preliminary action allows most classifications to be completed automatically without user intervention, minimizing processing time while still improving accuracy through targeted user feedback on borderline cases
Data Source
AI summary
An image processing device includes: a determination unit configured to determine, based on a feature amount of input image data, a category of the input image data and a score representing confidence of the category, with a classification model; a display unit configured to display an image representing the input image data and the determined category of the input image data; an acceptance unit configured to accept a correction of the displayed category from a user; and an updating unit configured to update the classification model, based on the correction of the category.