Semi-automatic Image Segmentation via Multi-scale Saliency
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Solution Overview
Problem
Existing image segmentation methods require significant user input and are inefficient due to high computational complexity and robustness issues, particularly in semi-automatic color image segmentation.
Innovation Solution
A semi-automatic image segmentation system using saliency detection by color histogram contrast, which employs the expectation-maximization algorithm for unsupervised classification and a multi-scale scheme to estimate object masks, reducing user inputs and improving stability and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If grab-cut method is used for color image segmentation, then segmentation accuracy is improved by incorporating smoothness term and local gradient analysis, but computational complexity increases and robustness deteriorates
Solution Approach 1:
The patent divides the image into multiple scales using a multi-scale scheme, processing the image at different resolution levels. This segmentation approach allows the algorithm to capture objects at various sizes while reducing computational burden at each scale compared to processing the full-resolution image directly with grab-cut.
Solution Approach 2:
The patent changes the parameter of image resolution by processing images at multiple scales rather than a single fixed resolution. This allows the system to adaptively select appropriate processing detail levels, reducing computational complexity while maintaining segmentation accuracy for objects of different sizes.
2Measurement precision
If grab-cut method is used for color image segmentation, then segmentation accuracy is improved by incorporating smoothness term, but robustness deteriorates
Solution Approach 1:
By segmenting the processing task across multiple scales, the patent reduces the impact of local variations and noise that can cause grab-cut to fail. The multi-scale approach provides redundancy and stability, making the segmentation more robust to different image conditions while maintaining accuracy.
3Device complexity
If traditional thresholding method is used for segmentation, then computational simplicity is maintained, but segmentation accuracy deteriorates for multi-colored objects
Solution Approach 1:
The patent applies a simplified version of grab-cut at multiple scales rather than using complex thresholding. This maintains relative computational simplicity while significantly improving accuracy for multi-colored objects through the multi-scale processing framework.
Solution Approach 2:
The patent applies a partial version of the full grab-cut algorithm at multiple scales rather than implementing the complete complex algorithm once. This partial application across scales achieves better accuracy with controlled computational cost.
Data Source
Figure 1A~1B
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Figure 2B
AI summary
Semi-automatic (instant) color image segmentation is performed on an input image for an object located near the point given by the user (e.g., touch screen contact). The size, shape, and the number of colors of the target object which is about (lying around or surrounding) the user-selected point is determined, and determinations made for which region the pixels belong to, either foreground or background. A binary object mask is generated which can be utilized for removing the material surrounding that object, or inverting the mask to remove the material of the object.