Digital Image Selection via Similarity Grouping
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Solution Overview
Problem
Existing digital image selection methods require user intervention, rely on generic aesthetic or quality standards, or necessitate additional devices to monitor user behavior, making it difficult to identify important images without burdening users or using extra equipment.
Innovation Solution
A method that automatically analyzes digital image collections to identify sets of similar images, selects the largest sets of similar images as important, and stores this selection in a processor-accessible memory, leveraging the fact that photographers often capture multiple versions of important images, without requiring user input or additional devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated image quality algorithms are used to rank images, then technical image quality is improved, but user-perceived importance deteriorates
Solution Approach 1:
The patent segments images into groups of similar images based on visual content analysis. By identifying and grouping similar images, the system can select one representative image from each group, ensuring that user-perceived important images (even if technically imperfect) are not overlooked. This segmentation approach resolves the contradiction by preserving images that users value while still applying automated quality assessment.
2Measurement precision
If user interaction monitoring devices are added to determine important images, then accuracy of identifying important images is improved, but device complexity deteriorates
Solution Approach 1:
The patent employs self-service by utilizing the device's existing sensors (accelerometer, gyroscope, GPS) and built-in processing capabilities to automatically determine image importance. The system analyzes shooting parameters, device orientation, and location data that are already captured during normal operation, eliminating the need for additional monitoring devices while maintaining high identification accuracy.
3Reliability
If multiple versions of important images are captured, then completeness of important images is improved, but quantity of images to manage deteriorates
Solution Approach 1:
The patent merges similar images by grouping them based on visual content analysis and selecting one representative image from each group. This merging process reduces the total number of images to manage while preserving the completeness of important content, as the selected representative image captures the essential subject matter that users value.
Data Source
AI summary
A method for selecting important digital images in a collection of digital images, comprising: analyzing the digital images in the collection of digital images to identify one or more sets of similar digital images; identifying one or more sets of similar digital images having the largest number of similar digital images; selecting one or more digital images from the identified largest sets of similar digital images to be important digital images; and storing an indication of the selected important digital image in a processor accessible memory.