Foreground Segmentation via Edge Knowledge Transformation
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Solution Overview
Problem
Existing image foreground segmentation methods based on deep learning require a large number of labeled samples and consume network capacity for category identification, limiting their effectiveness in segmenting images without category information.
Innovation Solution
An image foreground segmentation algorithm using edge knowledge transformation, which leverages open-source image datasets of non-target categories to perform foreground segmentation guided by few labeled samples of the target category through an edge-aware self-supervised module, employing affine transformations and adversarial networks to transform edge knowledge between categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based image foreground segmentation is used, then segmentation accuracy is improved, but a large number of labeled samples are required
Solution Approach 1:
The patent extracts and utilizes edge information from images as a separate feature representation, independent of category labels. By focusing specifically on edge detection and transformation rather than full category classification, the method reduces dependency on large labeled datasets while maintaining segmentation accuracy.
Solution Approach 2:
The patent introduces edge knowledge as an intermediary between images and segmentation results. Instead of directly learning from labeled images, the system transforms edge information from source images and uses it as intermediate knowledge to guide segmentation, reducing the need for extensive labeled training data.
2Measurement precision
If category identification is performed in segmentation network, then segmentation accuracy is improved, but network capacity is occupied
Solution Approach 1:
The patent removes the category identification component from the segmentation network, extracting only the essential edge information needed for segmentation. This simplifies the network architecture by eliminating unnecessary category classification layers while retaining core segmentation functionality.
Solution Approach 2:
Instead of using category information to guide segmentation, the patent inverts the approach by using edge information without category labels to achieve segmentation. This reverses the traditional pipeline where category identification precedes segmentation, allowing segmentation to occur based purely on edge characteristics.
3Quantity of substance
If unsupervised image segmentation is used, then labeled sample requirements are reduced, but segmentation control is lost
Solution Approach 1:
The patent implements self-supervised learning where the system automatically generates edge knowledge from source images without external labels. The edge detection and transformation processes are self-contained, allowing the system to serve itself by creating training signals from raw image data rather than requiring manual annotation.
Data Source
AI summary
An image foreground segmentation algorithm based on edge knowledge transformation includes the following steps: 1) construction of an image segmentation framework with edge self-supervised mechanism; 2) construction of an inner edge and outer edge discriminator; 3) generation of pseudo-segmented triplet data; 4) two edge adversary foreground segmentation guided by a very few labeled samples of the target category. According to the image foreground segmentation algorithm based on edge knowledge transformation established in the above steps, under the guidance of a very small number of labeled segmentation samples of the target category, the inner and outer edge discrimination network transforms edge knowledge of a large amount of open-source labeled data of a non-target category into a foreground segmentation network for the target category image by the way of adversary of segmentation results, and realize the segmentation of foreground target category objects of the image.