Trimap Segmentation Neural Network for Portrait Image Masking
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Solution Overview
Problem
Conventional image processing systems lack accuracy and efficiency in generating image masks for digital images, particularly due to reliance on single techniques and excessive user interactions, leading to low-quality results and limited flexibility across different object types.
Innovation Solution
A multi-branch pipeline system that classifies digital images into portrait or non-portrait categories, using separate neural networks for defined and blended boundary regions to generate accurate image masks, and automatically determines trimap segmentations without manual labeling, enhancing flexibility and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems utilize a single technique for generating image masks, then the system complexity is reduced, but the accuracy and quality of image masks deteriorate for certain object types
Solution Approach 1:
The system segments the image mask generation process into multiple specialized branches: a first branch for portrait images and a second branch for non-portrait images. Each branch contains techniques optimized for its specific object type, allowing the system to achieve high accuracy for both categories without excessive overall complexity through modular organization.
Solution Approach 2:
Different image mask generation techniques are applied to different regions of the image based on object type detection. The system identifies whether the image contains a portrait or non-portrait object and applies the locally optimal technique for that specific region, thereby improving overall accuracy without requiring a single complex universal technique.
2Adaptability or versatility
If conventional systems rely on excessive user interactions with graphical user interfaces to generate trimap segmentations, then the flexibility and adaptability are improved, but the productivity and efficiency deteriorate
Solution Approach 1:
The system implements automatic trimap segmentation generation using neural networks that self-adjust and optimize parameters without requiring user interaction. The neural network automatically learns from training data and adapts to different image types, providing both high flexibility and efficiency simultaneously by eliminating manual intervention while maintaining adaptability through automated learning.
Solution Approach 2:
The manual mechanical interaction process (user dragging sliders, adjusting parameters in graphical interfaces) is replaced with an automated neural network system that performs trimap segmentation automatically. This substitution maintains the adaptability of parameter adjustment while dramatically improving efficiency by eliminating the need for user interactions.
3Ease of operation
If conventional systems utilize excessive user interactions for parameter adjustment, then the ease of operation is improved, but the loss of time and computational overhead increase
Solution Approach 1:
The system performs preliminary training of neural networks on large datasets before actual image mask generation. During this preliminary phase, the network learns optimal parameters and patterns, so that during actual operation, no user interaction is needed and processing is immediate. This preliminary preparation eliminates both user interaction time and processing delays during actual use.
4Ease of manufacture
If conventional systems produce image masks without accurate labeling, then the ease of manufacture is improved, but the manufacturing precision and quality of image masks deteriorate
Solution Approach 1:
The manual labeling process is replaced with automated neural network-based labeling that achieves both ease of manufacture and high precision. The neural network automatically generates accurate labels by learning from training data, eliminating the need for manual user labeling while maintaining or improving label accuracy, thereby solving both ease and precision requirements simultaneously.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.