Feature Mask Determination Using Superpixel Graph Cut Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image editing technologies lack efficient methods for automatically and accurately determining feature masks in images, particularly for human bodies, which hinders automated image modification and processing efficiency.
Innovation Solution
A computer-implemented method that estimates prior regions in an image, constructs a graph based on superpixels, determines superpixel scores and edge scores, and applies a graph cut technique to segment regions, creating a feature mask that indicates the degree to which each pixel depicts a feature, such as a human body, allowing for precise image modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual image editing methods are used, then image modification can be performed, but processing efficiency is low and manual editing efforts are high
Solution Approach 1:
The system automatically determines feature masks and segments regions without requiring manual user input or intervention. The computer-implemented method performs self-service by autonomously identifying features, constructing graphs, and generating masks, thereby eliminating the need for manual editing and significantly improving processing efficiency.
Solution Approach 2:
The patent replaces manual mechanical editing processes with automated computational methods. Instead of manual image editing, the system uses graph-based algorithms, superpixel segmentation, and automated feature detection to perform image modification, substituting human labor with computational automation.
2Productivity
If automated feature mask determination methods are implemented, then processing efficiency improves, but measurement precision and accuracy of feature identification may deteriorate
Solution Approach 1:
The patent divides the image into multiple superpixels and constructs a graph where each node represents a superpixel. This segmentation approach allows the automated system to process images efficiently while maintaining accuracy by analyzing and segmenting regions based on color similarity and graph-cut techniques, ensuring precise feature boundary identification.
Solution Approach 2:
The system performs multiple passes of graph-cut segmentation and iteratively refines the feature mask determination. By applying the graph-cut technique repeatedly and adjusting parameters, the system ensures high accuracy in feature identification while maintaining automated processing speed, overcoming the trade-off between speed and precision.
3Measurement precision
If complex graph-based segmentation techniques are used, then feature mask accuracy improves, but device complexity and computational resources increase
Solution Approach 1:
The patent segments the image into superpixels before constructing the graph, reducing the number of nodes in the graph compared to using individual pixels. This pre-segmentation step simplifies the computational complexity while maintaining segmentation accuracy, as the graph operates on fewer, larger units rather than individual pixels.
Solution Approach 2:
The system performs preliminary actions by pre-processing the image to identify superpixels and estimate prior regions before applying the graph-cut technique. This preliminary organization of data structures and pre-computation of superpixel properties reduces the complexity of the subsequent graph-based segmentation, making the overall system more efficient.
Data Source
AI summary
Implementations relate to feature mask determination for images. In some implementations, a computer-implemented method to determine a feature mask for an image includes estimating one or more prior regions in the image that define a feature in the image. The method determines superpixels based on multiple pixels of the image similar in color. The method constructs a graph, each node of the graph corresponding to a superpixel, and determines a superpixel score for each superpixel based on a number of pixels of the superpixel. The method determines one or more segmented regions in the image based on applying a graph cut technique to the graph based at least on the superpixel scores, and determines the feature mask based on the segmented regions. The feature mask indicates a degree to which pixels of the image depict the feature. The method modifies the image based on the feature mask.