Interactive Assisted Segmentation for Image Labeling
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Solution Overview
Problem
The challenge lies in efficiently and cost-effectively labeling large datasets for machine learning models, particularly in image segmentation, where manual labeling is time-consuming, expensive, and prone to errors, and existing methods like Mask-RCNN are unsuitable for large images with many small objects.
Innovation Solution
A human-interactive method using a combination of unsupervised and semi-supervised techniques, where a human annotator labels a subset of pixels, and an ML model generates pseudo-labels for unlabeled pixels, allowing for iterative correction and retraining, reducing the time required for labeling large images by 24 to 48 times compared to traditional methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used for image segmentation, then labeling accuracy can be maintained, but labeling time and cost increase significantly
Solution Approach 1:
The system performs preliminary automated labeling using a pre-trained ML model before human annotators review the results. This preliminary action provides a starting point that is already reasonably accurate, reducing the time human annotators need to spend while maintaining high final labeling accuracy through subsequent correction and verification steps.
Solution Approach 2:
The patent introduces an intermediary ML model that acts as a bridge between automated labeling and human annotation. The model generates initial pseudo-labels that human annotators then correct, creating a collaborative workflow where the intermediary system handles the bulk of labeling work while humans provide refinement, thus reducing overall labeling time while maintaining accuracy.
2Productivity
If crowd-sourcing is used for labeling, then large volumes of data can be labeled, but labeling consistency and quality control become challenging and costly
Solution Approach 1:
The system implements feedback loops where the ML model is continuously retrained using corrected labels from human annotators. This feedback mechanism improves the model's performance over time, enabling it to generate more accurate pseudo-labels that require less human correction, thereby maintaining consistency across large datasets while preserving high labeling throughput.
Solution Approach 2:
The patent dynamically adjusts the threshold for accepting automated pseudo-labels based on model confidence scores. As the model improves through retraining, the system can accept higher-confidence automated labels with fewer human interventions, while lowering the threshold for uncertain cases. This parameter adjustment maintains labeling consistency across varying data types while preserving productivity.
3Productivity
If automated labeling is used, then labeling speed increases, but label accuracy decreases due to approximate labels
Solution Approach 1:
The system applies automated labeling selectively rather than universally - using high-confidence pseudo-labels for clear-cut cases while directing human annotation effort toward ambiguous or complex regions. This partial application of automated labeling maintains high speed for straightforward cases while ensuring accuracy for difficult cases, achieving both objectives simultaneously.
Data Source
AI summary
Subject matter regards improving image segmentation or image annotation. A method can include receiving, through a user interface (UI), for each class label of class labels to be identified by the ML model and for a proper subset of pixels of the image data, data indicating respective pixels associated with the class label, partially training the ML model based on the received data, generating, using the partially trained ML model, pseudo-labels for each pixel of the image data for which a class label has not been received, and receiving, through the UT, a further class label that corrects a pseudo-label of the generated pseudo-labels.