User Input Speed Drives Image Segmentation Training Data
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Solution Overview
Problem
Obtaining high-quality training data for image segmentation models is time-consuming and expensive, as existing methods require extensive offline training with many examples, and there is a need for a simpler and more effective way to gather training data for each image segmentation task.
Innovation Solution
The solution involves using user input speed to determine the width of an 'unknown area' around the region of interest in an image, where training data is extracted from this area to train a segmentation model, with faster input speeds indicating lower accuracy and thus using data further away from the boundary, and slower speeds using data closer to the boundary for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive offline training with many examples is used to obtain high-quality training data for image segmentation models, then the quality and accuracy of segmentation improves, but the time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by having users draw rough boundaries on images, which are then automatically refined into high-quality training data through the segmentation model. This preliminary user input serves as a foundation that accelerates the training process, eliminating the need for extensive manual annotation while still producing accurate training examples.
Solution Approach 2:
The segmentation model serves itself by automatically generating refined training data from rough user boundaries. The system uses its own capabilities to improve its training data without requiring external manual annotation for each example, thereby reducing time consumption while maintaining quality.
2Measurement precision
If extensive offline training with many examples is used to obtain high-quality training data for image segmentation models, then the quality and accuracy of segmentation improves, but the complexity and cost of the process increase
Solution Approach 1:
The training data generation process is segmented into two distinct stages: rough boundary drawing by users and automatic refinement by the segmentation model. This segmentation of the annotation process simplifies the overall workflow by dividing complex manual annotation into a simple user action followed by automated processing.
Solution Approach 2:
The segmentation model acts as an intermediary between rough user boundaries and high-quality training data. It mediates the transformation process, converting simple user inputs into refined training examples without requiring direct manual intervention for each training sample, thereby reducing process complexity.
3Ease of operation
If a fixed width area is used around the boundary for training data extraction, then the process is simple, but it cannot adapt to varying user input speeds and accuracy
Solution Approach 1:
The width of the training area is made dynamic rather than fixed. The system automatically adjusts the area width based on the speed at which users draw boundaries - slower speeds indicate higher accuracy and result in narrower training areas, while faster speeds result in wider training areas. This dynamic adaptation maintains process simplicity while accommodating varying user input characteristics.
Solution Approach 2:
The system changes the parameter of training area width based on user input speed. By monitoring how quickly users draw boundaries and adjusting the extraction area accordingly, the system adapts to different user behaviors and accuracy levels without complicating the overall process, maintaining ease of operation while improving adaptability.
Data Source
AI summary
An image segmentation apparatus comprises a processor which receives user input specifying a boundary of a region of interest of an image. The processor determines speed of the user input. A segmentation component maps the speed of the user input to a width of an area associated with the boundary of the region of interest. The segmentation component is configured to extract training data from the image on the basis of the area, and to train a segmentation model using the extracted training data. The segmentation component is configured to process image elements of the area using the trained segmentation model in order to compute an indication whether the image elements are to be assigned to the region of interest or not.