Camera Image Editing Recommendations From Learned User Preferences
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Solution Overview
Problem
Users face inconvenience in repeatedly performing similar image editing operations to achieve their preferred results, requiring time and effort, especially when capturing images with electronic devices.
Innovation Solution
An electronic device analyzes user preferences based on their usual patterns without direct interaction, using AI algorithms to automatically recommend edited images by identifying image classes and applying corresponding editing elements, storing preferences in a database for future use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users perform manual image editing operations to achieve preferred results, then image editing quality can be customized according to user preference, but time consumption and operational effort increase significantly
Solution Approach 1:
The system automatically analyzes user's historical editing patterns and preferences to generate recommended editing parameters without requiring manual input. The electronic device performs self-learning by detecting editing triggers and analyzing user preferences from previously edited images, then applies these learned patterns to automatically generate edited images, allowing the system to serve itself rather than requiring continuous user intervention
Solution Approach 2:
The system pre-analyzes and stores user editing preferences and patterns in advance by monitoring and analyzing historical editing operations. When a new image needs editing, the system has already prepared the recommended editing parameters based on pre-collected user preferences, eliminating the need for real-time manual editing decisions and reducing time consumption
2Reliability
If users repeatedly perform similar image editing operations to achieve consistent preferred results, then editing quality can be maintained according to user preference, but operational complexity and effort accumulate
Solution Approach 1:
The system continuously monitors user's editing operations and feedback to update and refine the stored user preferences. By detecting editing triggers and analyzing the differences between original and edited images, the system learns from user feedback and adjusts the recommended editing parameters to better match user expectations, ensuring consistent and reliable editing results over time
Solution Approach 2:
The system automatically maintains and updates the user preference database by analyzing historical editing operations without requiring user intervention. This self-learning mechanism ensures that the editing recommendations become increasingly accurate and consistent with user preferences, maintaining reliability while reducing operational effort
Data Source
AI summary
Disclosed in various embodiments of the present disclosure provide an image in an electronic device. An electronic device according to various embodiments includes 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.