Video Editing Propagation via Manifold Geodesic Distance
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Solution Overview
Problem
Current image and video editing methods are inefficient, particularly in handling color transitions and complex scenes, requiring extensive manual intervention and struggling with semi-transparent regions and object shape susceptibility.
Innovation Solution
A method that maps pixels to a selected feature space, uses Locally Linear Embedding (LLE) for dimension reduction, and propagates user-specified editing requests to preserve local feature structures, applicable in various applications like color transformation, cloning, and matting, using RGB, RGBXYT, or Intensity-SIFT-Texture-Coordinate feature spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If diffusion map-based editing propagation is used, then editing can be automated, but it is inefficient and cannot effectively reflect non-affinity among pixels
Solution Approach 1:
The patent changes the parameter for measuring pixel affinity from diffusion distance to geodesic distance on a manifold. This parameter change enables the system to capture non-affinity relationships between pixels, improving editing efficiency while maintaining automation. The geodesic distance on a learned manifold provides a more accurate measure of pixel relationships than Euclidean diffusion distance.
Solution Approach 2:
The patent replaces the mechanical diffusion process with a manifold-based geodesic distance calculation. Instead of using simple diffusion maps that treat all pixels uniformly, the system substitutes a more sophisticated manifold learning approach that models the intrinsic geometry of image data, enabling efficient and accurate editing propagation.
2Ease of manufacture
If global color transformation method is used, then color transformation is simple and effective, but user must specify reference for complex images
Solution Approach 1:
The patent implements self-service by automatically learning the manifold structure from the image data itself without requiring user-provided references. The system autonomously captures the intrinsic relationships between pixels and objects through manifold learning, eliminating the need for users to manually specify reference images or regions while maintaining simple and effective color transformation.
3Manufacturing precision
If Poisson equation-based cloning is used, then object merging is appropriate, but time and space consumption is high
Solution Approach 1:
The patent substitutes the computationally intensive Poisson equation solver with a manifold-based geodesic distance approach. This replacement maintains the accuracy of object merging by using geodesic distances to measure pixel affinities, while significantly reducing the time and space consumption associated with solving large-scale Poisson equations.
4Productivity
If mean-value coordinates cloning is used, then time and space consumption is reduced, but the method is susceptible to object shape
Solution Approach 1:
The patent changes the underlying parameter for measuring pixel relationships from mean-value coordinates to geodesic distance on a learned manifold. This parameter change enables the system to maintain high processing speed while achieving robustness to object shape variations, as the manifold structure captures intrinsic geometric relationships that are invariant to shape transformations.
5Manufacturing precision
If spectral matting is used, then matting accuracy is improved, but foreground extraction in semi-transparent regions remains poor
Solution Approach 1:
The patent substitutes spectral matting with a manifold-based approach that uses geodesic distances to model pixel relationships. This substitution improves foreground extraction in semi-transparent regions by capturing the intrinsic manifold structure of image data, which provides more reliable guidance for separating foreground and background objects compared to spectral methods.
6Ease of manufacture
If gray matching colorization is used, then gray images can be colorized, but complex scenes cannot be colorized well and require too much interaction
Solution Approach 1:
The patent substitutes gray matching colorization with manifold-based colorization that leverages the intrinsic geometric structure of color space. This substitution enables high-quality colorization of complex scenes by using geodesic distances to preserve local color relationships, while reducing the need for user interaction compared to traditional gray matching methods.
Data Source
AI summary
The invention discloses a method for editing propagation of video and image content based on local feature structure preservation, comprising: mapping all pixels in the input original image and/or video key frames to a selected feature space; finding K nearest neighbor pixels for each pixel according to feature vectors' Euclidean distance in the selected feature space; using Locally Linear Embedding (LLE) dimension reduction to construct the locally linear relationship between each pixel and its K nearest neighbor pixels in the selected feature space; According to the present invention, it is possible to accurately perform such image or video processing as automatic color transformation, interactive color editing, gray image colorization, video cloning and image matting.