Automated Content Labeling System with Superpixel Segmentation
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Solution Overview
Problem
Current automated approaches to annotating images for AI system training are inefficient and prone to inconsistencies due to variations in image quality, labeler subjectivity, and data processing capabilities, making it difficult and costly to generate large volumes of good quality training data.
Innovation Solution
An automated content labeling system that integrates human workforce management, a powerful API for integration and extensibility, and quality assurance tools like consensus and benchmarks scoring to ensure high-quality labeling, along with model-assisted and real-time human-in-the-loop labeling workflows for efficient data annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to ensure high quality training data, then labeling accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces an automated annotation system that acts as an intermediary between raw images and human labelers. The system pre-processes images using computer vision algorithms to generate initial annotations, which are then refined by human workers. This intermediary automated processing step reduces the time and effort required for manual annotation while maintaining high accuracy standards.
Solution Approach 2:
The system performs preliminary automated annotation before human review. By pre-processing images and generating initial labels using automated algorithms, the system prepares the data in advance, allowing human workers to focus only on verifying and correcting annotations rather than creating them from scratch. This preliminary action significantly reduces overall time consumption.
2Productivity
If fully automated annotation is used to reduce time and cost, then productivity is improved, but labeling accuracy deteriorates due to mis-labeling
Solution Approach 1:
The patent divides the annotation process into distinct segments: automated pre-processing, human verification, and quality review. Each segment handles specific tasks with appropriate tools and expertise. The automated segment handles initial label generation to maintain productivity, while human segments handle accuracy-critical verification and correction tasks.
Solution Approach 2:
The system implements a feedback loop where human-verified annotations are fed back to retrain and improve the automated annotation algorithms. This continuous feedback mechanism allows the system to learn from human corrections, progressively improving its accuracy while maintaining high productivity. The feedback ensures that automated processing does not compromise long-term labeling quality.
3Measurement precision
If multiple human labelers are used to improve accuracy through consensus, then labeling quality is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The system introduces an automated intermediary that manages the coordination between multiple human labelers. Rather than requiring complex manual coordination, the automated system assigns tasks, collects annotations, resolves conflicts through predefined consensus algorithms, and manages quality control. This intermediary automation reduces the coordination overhead and system complexity while maintaining the benefits of multiple reviewers.
4Reliability
If extensive quality review processes are implemented to ensure enterprise-grade data, then data quality is improved, but processing time and operational complexity increase
Solution Approach 1:
The patent implements local quality control where not all annotations undergo the same level of review. Instead, quality review intensity is adjusted based on local factors such as annotation confidence scores, image difficulty, and historical error rates. High-confidence annotations from reliable labelers receive minimal review, while uncertain or complex annotations receive more intensive review. This localized approach maintains high data quality while preserving processing speed.
Data Source
AI summary
An automated content labeling system is disclosed. An example embodiment is configured to: register a plurality of labelers to which annotation tasks are assigned; populate a labeling queue with content data to be annotated; assign annotation tasks from the labeling queue to the plurality of labelers; and provide a superpixel annotation tool enabling the plurality of labelers to configure a size of a segment cluster in an image of the content data, and select each segment cluster to be included in a segmentation feature with a specified object class, the segment clusters including similarly colored pixels from the image.