Index Element Database for Automated Image Recognition Training
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Solution Overview
Problem
The high cost and time-consuming process of obtaining and labeling training data for image recognition models, particularly in object detection, due to the need for manual filtering and labeling of public data, which is labor-intensive and prone to errors.
Innovation Solution
Creating an index element database with feature data from multiple images, allowing for the rapid extraction and matching of similar feature data to generate training images without manual labeling, using existing object detection models to annotate and extract features from unlabeled images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual filtering and labeling of public data is used to obtain training data, then the quality and accuracy of training data can be ensured, but the time consumption and labor costs increase significantly
Solution Approach 1:
The patent uses pre-built index element databases containing feature data from existing images as templates. Instead of manually creating all training data, the system copies and adapts feature data from the index database to generate new training images, significantly reducing manual labeling time while maintaining quality through the structured feature matching process
Solution Approach 2:
The patent performs preliminary actions by pre-building index element databases with extracted feature data from multiple images before actual training data generation is needed. This advance preparation creates a reusable resource that accelerates subsequent training data production without compromising quality
2Measurement precision
If manual labeling is performed to annotate training images, then accurate labels can be obtained, but labor costs and error rates increase
Solution Approach 1:
The system performs self-service by automatically extracting feature data from images and generating corresponding labels through the index element database matching process. The automated feature extraction and matching algorithms replace manual human labeling, eliminating labor costs and human errors while maintaining consistent accuracy
Solution Approach 2:
The patent replaces the mechanical human labeling process with automated computational systems. Feature extraction algorithms and database matching mechanisms substitute for manual human inspection and annotation, achieving both high accuracy and efficiency through systematic automated processing
3Quantity of substance
If extensive manual filtering and processing of public data is conducted, then comprehensive training data can be obtained, but the complexity and resource requirements increase
Solution Approach 1:
The index element database serves multiple functions: it stores feature data for matching, provides templates for generating training images, and enables efficient retrieval during training data production. This multi-functional structure reduces the need for separate processing systems while generating large volumes of training data with standardized complexity
Data Source
AI summary
The present disclosure relates to a method and a device for training an image recognition model and a related device. The method includes: extracting sub-image feature data from a detection frame sub-image of an input image; determining element feature data matching the sub-image feature data from an index element database; and outputting images related to the element feature data as training images for training the image recognition model. The index element database is built in advance based on a plurality of element feature data extracted from a plurality of candidate images.