Object Relationship Graph Editing for Context-Aware Image Effects
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Solution Overview
Problem
Existing image editing technologies lack the ability to automatically apply image effects based on the relationships between objects within an image, leading to suboptimal editing results.
Innovation Solution
An artificial intelligence system that utilizes a Graph Neural Network (GNN) model to analyze object relationships and apply image effects accordingly, generating a modified image that enhances the overall image quality by considering the interactions and features of the objects within the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rule-based systems are used for image editing, then the editing process is simple and controllable, but the system cannot automatically learn and adapt to user preferences
Solution Approach 1:
The patent replaces rule-based mechanical editing systems with an AI-based system that automatically learns user preferences and image characteristics. The system uses machine learning models to analyze images and apply appropriate effects without manual parameter setting, substituting the mechanical rule-following approach with intelligent automatic decision-making.
Solution Approach 2:
The AI system performs self-learning and self-adjustment by automatically analyzing images and user feedback to improve its editing capabilities. The system serves itself by continuously learning from data without requiring explicit programming for each editing scenario, enabling automatic adaptation to different user preferences and image types.
2Manufacturing precision
If general image effects are applied without considering object relationships, then the editing process is fast and simple, but the editing results do not suit the specific image content
Solution Approach 1:
The patent segments the image into multiple objects and analyzes the relationships between them. By dividing the image processing task into object-level segments, the system can apply tailored effects to each object based on its characteristics and relationships with other objects, improving editing precision without requiring processing the entire image as a single unit.
Solution Approach 2:
The system applies different image effects to different objects based on their local characteristics and relationships. Instead of applying a uniform effect to the entire image, the AI analyzes each object's context and applies customized effects locally, ensuring that the editing precision matches the specific requirements of each object while maintaining overall processing efficiency.
3Ease of operation
If manual parameter setting is required for each image effect, then the editing control is precise, but the user effort and time consumption increase
Solution Approach 1:
The AI system automatically performs parameter setting and effect selection based on image analysis, eliminating the need for manual user input. The system serves itself by learning optimal parameters from training data and automatically adjusting them based on the specific image content and user preferences, reducing user effort while maintaining or improving editing precision.
Solution Approach 2:
The system incorporates user feedback mechanisms where user preferences and corrections are fed back into the AI model to improve future parameter settings. This feedback loop allows the system to learn from user interactions and automatically adjust its parameter selection, reducing manual effort while maintaining high precision in editing control over time.
Data Source
AI summary
An electronic device includes: a processor; and a memory storing instructions. By executing the instructions, the processor is configured to: receive a first image, recognize a plurality of objects in the first image to generate object information representing the plurality of objects, generate an object relationship graph including relationships between the plurality of objects, based on the first image and the object information, obtain image effect data including image effects to be respectively applied to the plurality of objects by inputting the object relationship graph to an image modification Graph Neural Network (GNN) model, and generate a modified image based on the first image, the object information, and the image effect data.