Preference-Learned Image Editing Recommendations During Capture
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Solution Overview
Problem
Users face inconvenience and repetitive effort in editing images according to their preferences, requiring repeated image editing operations for each image capture, which is time-consuming and inefficient.
Innovation Solution
An electronic device analyzes user preferences based on usual patterns without direct interaction, using a learning model to automatically provide recommended edited images by identifying image classes and applying corresponding editing elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually edit images according to their preferences each time, then the editing results match user preferences, but the time and effort required for editing increases
Solution Approach 1:
The system performs preliminary analysis of user editing patterns and preferences during the image capture process, preparing recommended editing elements before the user needs to make decisions. This allows the system to proactively suggest edits rather than requiring users to manually adjust each parameter, reducing editing time while maintaining quality.
Solution Approach 2:
The system automatically analyzes user preferences from past editing behavior and generates recommended editing elements without requiring direct user input for each image. The system serves itself by learning from user patterns and autonomously providing personalized editing suggestions, freeing users from repetitive manual editing tasks.
2Measurement precision
If users perform repeated editing operations for each image, then consistent editing quality is achieved, but the complexity of the editing process increases
Solution Approach 1:
The system copies successful editing patterns from past user behavior and applies them to new images. By replicating proven editing approaches rather than requiring users to recreate them each time, the system maintains consistency while simplifying the process. The learning model stores and reuses effective editing configurations across multiple images.
Solution Approach 2:
The system dynamically adjusts editing parameters based on learned user preferences and image characteristics. Instead of requiring users to manually set each parameter, the system automatically modifies parameters like brightness, contrast, and saturation based on the analyzed preferences, reducing process complexity while maintaining consistent quality through data-driven parameter selection.
3Productivity
If the system automatically analyzes user preferences, then editing efficiency improves, but the need for direct user interaction decreases
Solution Approach 1:
The system implements feedback loops where user responses to recommended editing elements are analyzed and used to refine future recommendations. By continuously learning from user feedback, the system improves its ability to predict preferences accurately, increasing editing efficiency while requiring minimal direct interaction. The feedback mechanism allows the system to adapt to changing user preferences over time.
Solution Approach 2:
The system performs self-service by automatically analyzing user preferences from captured images and past editing behavior without requiring direct user input. The learning model autonomously processes image data, identifies patterns, and generates personalized editing recommendations, improving efficiency while reducing the burden of manual interaction. Users simply need to provide initial preference examples, after which the system operates independently.
Data Source
AI summary
Disclosed in various embodiments of the present disclosure are a method and an apparatus for providing an image in an electronic device. An electronic device according to various embodiments comprises a camera module, a display, a memory, and a processor, where the processor can display a preview image through the display, capture an image at least based on of the preview image in response to a user input while displaying the preview image, perform image analysis based on the captured image, identify at least one class related to the captured image based on the image analysis result, identify at least one user preference based on the identified class, and provide, through the display, at least one recommended image related to the at least one user preference.