Automatic Digital Image Curation via Quality Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in curating large collections of digital images due to the difficulty in identifying and selecting high-quality images based on technical specifications and visual appeal, with existing methods failing to efficiently differentiate between good and bad images.
Innovation Solution
A computer-implemented method and system for automatic curation of digital images, which selects duplicate images, calculates an image quality score based on content and metadata, and associates a ranking with each image, allowing for the identification and sharing of high-quality images while providing options for hypothetical adjustments to improve image quality scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually curate digital images based on technical specifications and visual appeal, then image quality selection accuracy is improved, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables automatic self-curation of digital images by using machine learning models to evaluate image quality and generate rankings without requiring manual user intervention for each evaluation. The system processes images autonomously based on learned patterns from training data.
Solution Approach 2:
The patent replaces manual mechanical image selection processes with automated computational systems that use neural networks and machine learning algorithms to evaluate and rank images, substituting human cognitive processing with automated digital systems.
2Ease of operation
If existing image evaluation methods are used, then ease of operation is maintained, but ability to differentiate between good and bad images deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where users can provide feedback on image quality assessments, and this feedback is used to retrain and refine the machine learning models, continuously improving the system's ability to differentiate between good and bad images while maintaining ease of use.
Solution Approach 2:
The patent changes the parameters of image evaluation by introducing multiple quality dimensions and weighted scoring mechanisms that allow the system to adapt to different user preferences and image types, improving differentiation capability while maintaining operational simplicity.
3Productivity
If automatic image curation is implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The system segments the complex image curation task into distinct modules: image processing, quality evaluation, ranking generation, and user interface presentation. Each module handles a specific function independently, making the overall complex system more manageable and maintainable while achieving high productivity.
Data Source
AI summary
The disclosed subject matter relates to computer implemented methods for automatic curation of digital images. In one aspect, the method includes selecting one of every two or more duplicate digital images of a plurality of digital images. The method further includes calculating an image quality score for each of the selected digital images. The method further includes associating a ranking with each of the selected digital images based on its respective calculated image quality score.