Image Labeling Pipeline Using Pseudo Labels and Human Review
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for labeling image data are cumbersome and expensive, hindering the efficient utilization of vast image datasets for machine learning, particularly due to the inefficiencies in selecting, bounding, and labeling image aspects.
Innovation Solution
A computer-based system and method for image labeling that includes receiving images, selecting areas with bounding boxes, cropping, storing, filtering incorrectly identified objects, generating pseudo labels, and assigning correct item names, utilizing neural networks for training and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human labeling services are used to label image data, then labeling accuracy can be maintained, but the cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary automated labeling using neural networks before human review, pre-processing the image data to identify objects and generate initial labels. This preliminary action reduces the subsequent time required for human verification while maintaining high accuracy standards.
Solution Approach 2:
An automated neural network labeling system serves as an intermediary between raw image data and final labeled output. This intermediary performs initial labeling and filtering, reducing the burden on human labelers and accelerating the overall process while maintaining quality through subsequent human verification of critical cases.
2Productivity
If automated labeling methods are used, then processing speed increases, but labeling accuracy and reliability decrease
Solution Approach 1:
The system implements feedback loops where automated labeling results are continuously evaluated and refined. Neural networks learn from verified labeled data, and their predictions are fed back into the training process, progressively improving both speed and reliability through iterative optimization based on performance feedback.
Solution Approach 2:
The system dynamically adjusts labeling parameters such as confidence thresholds, object detection sensitivity, and verification requirements based on data characteristics and performance metrics. This allows the system to optimize the balance between processing speed and reliability for different types of image data and labeling tasks.
3Measurement precision
If comprehensive manual verification of each image aspect is performed, then labeling quality is ensured, but the complexity and cost of the labeling process increase
Solution Approach 1:
Instead of verifying every single image aspect manually, the system applies partial verification focused on critical cases where automated confidence is low or uncertainty is high. This selective verification approach maintains labeling quality while significantly reducing process complexity compared to comprehensive manual review of all images.
Solution Approach 2:
The automated neural network system performs self-verification through confidence scoring and internal consistency checks. The system autonomously identifies and flags only those cases requiring human review, reducing the need for complex manual verification processes while maintaining quality standards through the network's self-evaluation capabilities.
Data Source
AI summary
The systems and methods disclosed herein provide a computer system, the computer system configured for receiving a plurality of images, selecting an area of at least of the images defined by a bounding box, cropping the selected areas from the images and storing the cropped images in folders, filtering incorrectly identified objects, generating pseudo labels for the remaining images, and assigning correct item names for the pseudo labels.