Synthesized Frame Segmentation Network Training
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Solution Overview
Problem
Existing image segmentation methods, especially those relying on convolutional neural networks (CNNs), face challenges in obtaining sufficient tagged training data, which is time-consuming and resource-intensive, particularly for specific frames or scenes.
Innovation Solution
The method synthesizes training data by associating primary elements in a scene with background and subject tags, generating textures, and combining them into a synthesized frame for overtraining a segmentation network, allowing it to effectively segment specific frames or scenes with minimal human effort and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks are used for image segmentation, then segmentation accuracy is improved, but the need for extensive tagged training data increases
Solution Approach 1:
The patent uses texture synthesis to create synthetic training images that copy the statistical properties and visual characteristics of real images. By generating artificial training data through texture modeling and patch synthesis, the system eliminates the need for manually tagged real images while maintaining the statistical patterns necessary for CNN training, thus resolving the contradiction between accuracy and data quantity requirements
Solution Approach 2:
The system performs self-service by automatically generating its own training data through texture analysis and synthesis. Instead of requiring external tagged datasets, the algorithm extracts texture information from input images and synthesizes training samples autonomously, making the training process self-sufficient and eliminating the bottleneck of manual data annotation
2Reliability
If manually tagged training data is collected, then network training quality is improved, but time consumption and resource requirements increase
Solution Approach 1:
Rather than manually collecting and tagging training images, the system copies the essential statistical properties and texture characteristics of real images through automated texture synthesis. This approach maintains training quality by preserving the underlying data distribution while eliminating the time-consuming manual annotation process
Solution Approach 2:
The system performs preliminary texture analysis and model fitting on a small set of example images before generating the full training dataset. By pre-computing texture parameters and statistical models from minimal input, the system prepares the training data structure in advance, significantly reducing the overall time required for data preparation while maintaining training quality
3Measurement precision
If the segmentation network is trained on a specific frame, then segmentation accuracy for that frame is improved, but the network becomes overtrained and cannot generalize to other frames
Solution Approach 1:
The system changes the parameters of the training data by synthesizing images with varied texture parameters, lighting conditions, and spatial configurations. By generating training samples with diverse parameter variations while maintaining the same underlying scene structure, the network learns frame-specific patterns without becoming overly specialized, enabling both accuracy and generalization across frames
Solution Approach 2:
The texture synthesis approach creates a universal training framework that can be applied to any frame within the same scene. By modeling the statistical properties of the scene in a frame-agnostic manner and generating synthetic variations, the system enables the network to learn universal scene characteristics that transfer across multiple frames, achieving both specific frame accuracy and broader generalization
Data Source
AI summary
Systems and methods for frame and scene segmentation are disclosed herein. One method includes associating a first primary element from a first frame with a background tag, associating a second primary element from the first frame with a subject tag, generating a background texture using the first primary element, generating a foreground texture using the second primary element, and combining the background texture and the foreground texture into a synthesized frame. The method also includes training a segmentation network using the background tag, the foreground tag, and the synthesized frame.