Image Segmentation Update via Weighted User Input Feedback
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Solution Overview
Problem
Existing image segmentation techniques require significant user input and may not effectively update segmentation based on user corrections, leading to suboptimal results in distinguishing foreground from background.
Innovation Solution
The method updates image segmentation by giving more weight to user-identified image elements, allowing for iterative refinement of foreground and background properties using weighted combinations and post-processing to ensure connectivity of updated regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image segmentation techniques are used, then initial segmentation can be achieved, but the segmentation accuracy deteriorates when user corrections are needed
Solution Approach 1:
The system implements a feedback mechanism where user corrections to segmentation are processed and used to iteratively improve the segmentation model. When users provide correction input, the system updates the segmentation based on this feedback, allowing continuous refinement of accuracy without requiring complete re-segmentation.
Solution Approach 2:
The system changes the weighting parameters of different image regions based on user input. By adjusting the importance weight of user-corrected regions versus automatically segmented regions, the system adapts the segmentation algorithm to prioritize user corrections, thereby improving overall accuracy while maintaining ease of operation.
2Measurement precision
If user input is given to correct segmentation errors, then segmentation accuracy improves, but the time required for processing increases
Solution Approach 1:
Instead of requiring complete re-segmentation of the entire image when corrections are needed, the system applies partial action by updating only the affected regions and their immediate neighborhoods. This selective updating approach maintains accuracy improvements while significantly reducing processing time compared to full re-segmentation.
Solution Approach 2:
The system performs preliminary processing by pre-calculating and storing region importance weights and connectivity information before user corrections are applied. This allows the correction process to proceed more efficiently using pre-prepared data structures, reducing the time required for iterative updates while maintaining high accuracy.
3Adaptability or versatility
If image elements are updated based on user input, then segmentation reflects user intentions, but the complexity of the updating process increases
Solution Approach 1:
The updating process is segmented into distinct modular steps: (1) identifying user-input regions, (2) calculating region importance weights, (3) updating properties of affected regions, and (4) propagating changes through connectivity relationships. This segmentation of the updating process into manageable modules reduces overall complexity while maintaining adaptability to user intentions.
Solution Approach 2:
The system applies local quality by treating different regions of the image differently during updating. Regions with higher importance weights (such as those with user input or high connectivity to user-input regions) receive updated properties, while regions with lower weights maintain their original properties. This selective updating simplifies the overall process complexity while preserving adaptability to user corrections.
Data Source
AI summary
Methods of updating image segmentation following user input are described. In an embodiment, the properties used in computing the different portions of the image are updated as a result of one or more user inputs. Image elements which have been identified by a user input are given more weight when updating the properties than other image elements which have already been assigned to a particular portion of the image. In another embodiment, an updated segmentation is post-processed such that only regions which are connected to an appropriate user input are updated.