Image Labeling Complexity Modeling for Resource and Cost Estimation
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Solution Overview
Problem
Current labeling services in service provider networks lack accurate estimation of resources and costs for image dataset labeling tasks, which are influenced by the complexity of the image data, cognitive effort required, and user-defined quality and timing requirements.
Innovation Solution
A system that utilizes machine learning models to determine data complexity, cognitive complexity, and product complexity to estimate the resources and costs for image labeling, providing multiple options to users based on historical data and labeler capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current labeling services are used without complexity estimation, then the labeling process can be completed, but accurate prediction of time and effort is not available
Solution Approach 1:
The system performs preliminary analysis of image datasets by calculating complexity metrics (data complexity, cognitive complexity, product complexity) before the actual labeling process begins. This allows accurate prediction of time and effort requirements in advance, enabling users to plan resources and expectations beforehand without adding complexity during execution.
Solution Approach 2:
The patent introduces complexity estimation as an intermediary layer between the user's labeling requests and the actual labeling execution. By inserting this estimation mechanism, the system provides accurate time and effort predictions without directly complicating the core labeling functionality, thus resolving the contradiction between measurement precision and device complexity.
2Manufacturing precision
If more resources are allocated to labeling tasks, then labeling quality can be improved, but cost increases
Solution Approach 1:
The system changes the parameters of resource allocation by using complexity metrics to determine the optimal amount of human resources needed. Instead of allocating fixed or maximum resources, the system calculates resource requirements based on data complexity, cognitive complexity, and product complexity, thereby improving labeling quality while optimizing costs through parameter-driven resource adjustment.
Solution Approach 2:
The patent implements feedback mechanisms where actual labeling performance is compared against predicted time and effort estimates. This feedback loop allows the system to refine future predictions and adjust resource allocation strategies, enabling improved labeling quality at optimized costs by learning from actual performance data.
3Productivity
If labeling time requirements are reduced, then productivity increases, but labeling quality may deteriorate
Solution Approach 1:
The system makes resource allocation dynamic by adjusting the number of labelers and time allocation based on real-time complexity assessment. For simple datasets with low complexity metrics, the system allocates fewer resources and shorter timeframes, achieving high productivity. For complex datasets, it automatically increases resources and time, maintaining quality. This dynamic adjustment resolves the contradiction between productivity and quality.
Data Source
AI summary
This disclosure describes a system that models the complexity of image labeling tasks utilizing image data, labeling instructions, and label requirements. A labeling service of a service provider network includes an application that determines a task complexity value based on a data complexity value, a cognitive complexity value, and a product complexity value. The task complexity value is used to predict or estimate needed resources for an image labeling task for labeling image data, e.g., the time and effort needed to label (annotate) the image data. Once the needed resources are estimated, associated costs may also be estimated. The needed resources and associated costs may be provided to a user that submitted the image data, who may then provide an indication with respect to proceeding with the image labeling task.