Semi-Automatic Image Labeling with GrabCut and Edge Tracking
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Solution Overview
Problem
The existing grabcut algorithm for image segmentation is inefficient in handling complex foreground shapes, leading to large frame ranges and decreased precision, and lacks a reliable method for correcting erroneously selected areas, resulting in suboptimal labeling data for neural network training.
Innovation Solution
A high-precision semi-automatic image data labeling method that executes the grabcut algorithm multiple times to refine the outline coordinates of the foreground image, incorporates an edge tracking algorithm to update local coordinates, and provides a correction mechanism for erroneously selected areas, allowing for real-time manual intervention and improved precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the grabcut algorithm is used to segment the foreground with a single frame drawn by the user, then the segmentation process is simple and fast, but the precision of segmentation decreases when the foreground has complex contour shapes, resulting in large frame ranges and inclusion of background pixels
Solution Approach 1:
The patent divides the segmentation process into multiple iterative stages. In each iteration, the algorithm segments the foreground based on current frame coordinates, then uses edge tracking to refine the boundary. This multi-stage segmentation approach allows the system to handle complex foreground shapes while maintaining reasonable processing speed through automated refinement steps.
Solution Approach 2:
The patent performs preliminary edge tracking on the binarized image obtained from grabcut segmentation. By pre-processing the segmented image to extract edge coordinates before final labeling, the system prepares refined boundary information in advance, which improves the accuracy of the final segmentation result without significantly increasing overall processing time.
2Device complexity
If the traditional grabcut algorithm is used without correction functions, then the processing workflow is simple, but the reliability of labeling data decreases when segmentation errors occur, allowing background images to be included near target image edges
Solution Approach 1:
The patent implements a correction mechanism that provides feedback loops for error detection and correction. After initial segmentation, the system allows users to identify erroneously selected areas, and automatically corrects these errors by re-executing the grabcut algorithm on the corrected regions. This feedback-based correction ensures high reliability of labeling data while keeping the additional complexity manageable through automated correction procedures.
3Measurement precision
If manual correction of segmentation errors is performed, then the precision of labeling data is improved, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The patent enables users to perform self-correction of segmentation errors through an intuitive interface. When users identify erroneously selected areas, the system automatically executes the correction process by re-running grabcut on the specified regions and updating the coordinate sets. This self-service correction mechanism allows users to improve labeling precision without requiring expert knowledge or significant time investment, as the complex computational tasks are automated.
Data Source
AI summary
Disclosed are a high-precision semi-automatic image data labeling method, an electronic apparatus and a non-transitory computer-readable storage medium. The high-precision semi-automatic image data labeling method may include: displaying a to-be-labeled image, the to-be-labeled image comprising a selected area and an unselected area; acquiring a coordinate point of the unselected area and a first range value; executing a grabcut algorithm based on the coordinate point of the unselected area and the first range value acquired, and obtaining a binarized image divided by the grabcut algorithm; executing an edge tracking algorithm on the binarized image to acquire current edge coordinates; updating a local coordinate set based on the current edge coordinates acquired; updating the selected area of the to-be-labeled image based on the local coordinate set acquired.