Skip Areas for Machine Learning Image Labeling
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Solution Overview
Problem
The manual labeling of images for machine learning models is resource-intensive, particularly when images contain difficult-to-label areas such as poor quality regions, occlusions, or repetitive features, leading to inefficiencies in training and evaluation.
Innovation Solution
The implementation of 'skip areas' in images, where challenging regions are excluded from labeling and training, allowing for the creation of partially labeled images that focus on non-skip areas for improved model training and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual labeling is performed on all image areas including difficult regions, then completeness of labeling is improved, but labeling time and resource consumption increase significantly
Solution Approach 1:
The patent divides the image into multiple regions based on labeling difficulty characteristics. Easy regions are identified and separated from difficult regions (such as occluded areas, low-resolution areas, or regions with repetitive patterns). Labelers only need to focus on the easy regions, while difficult regions are automatically handled or excluded, thus reducing overall labeling time while maintaining acceptable completeness.
Solution Approach 2:
Different quality requirements are applied to different regions of the image. High-quality manual labeling is applied only to regions where it provides significant value (easy regions), while difficult regions accept lower quality or automated labeling. This differential approach optimizes the balance between labeling completeness and time investment.
2Manufacturing precision
If all image regions are labeled manually, then data quality is improved, but resource consumption increases
Solution Approach 1:
Instead of labeling all regions exhaustively, the patent applies partial action by selectively labeling only the most valuable regions (easy regions with clear features). The difficult regions are either skipped or handled with automated methods, achieving sufficient data quality for model training without the excessive resource consumption of complete manual labeling.
Solution Approach 2:
The patent introduces an intermediary classification step that identifies and categorizes regions by labeling difficulty before the actual labeling process. This intermediary system (automated detection of easy/difficult regions) acts as a mediator that directs human labelers to only the regions where their input provides the best return on investment, improving resource efficiency.
3Reliability
If difficult-to-label areas are included in training data, then model robustness is improved, but training accuracy deteriorates due to labeling errors
Solution Approach 1:
The patent extracts and removes difficult-to-label regions from the set of regions requiring manual annotation. By taking out these problematic areas (occluded objects, blurry regions, etc.), the system avoids introducing labeling errors into the training data, thereby maintaining higher labeling accuracy for the regions that are actually labeled.
Solution Approach 2:
For difficult regions that are excluded from manual labeling, the patent accepts lower-quality or automated labels as disposable approximations. These regions are included in training data with the understanding that their labels are not as reliable, but they provide sufficient diversity for model robustness without compromising overall training accuracy.
Data Source
AI summary
An approach is provided for using one or more skip areas to label, train, and/or evaluate a machine learning model. The approach, for example, involves specifying the one or more skip areas with respect to an image. By way of example, a non-skip area of the image is a portion of the image that is not in the one or more skip areas. The approach also involves initiating a labeling of one or more features in the non-skip area of the image while excluding the one or more skip areas from the labeling to create a partially labeled image. The partially labeled image is then included in a training dataset for training a machine learning model.