Image Candidate Determination for Uniform Representation
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Solution Overview
Problem
Existing image management systems for events like graduation ceremonies struggle to ensure uniform representation of individuals in photo albums, leading to potential unfairness and difficulty in selecting images for deletion or public/private designation, as energetic children are overrepresented while shy children are underrepresented.
Innovation Solution
An image candidate determination apparatus that groups images by individuals and determines extraction or non-extraction candidates based on a total image evaluation value and a limit value for the number of images per person, using face recognition and evaluation scoring to balance representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If images are selected based on simple counting or random selection, then the selection process is simple and fast, but the representation uniformity of each person deteriorates
Solution Approach 1:
The system changes the parameter of image selection from simple counting to evaluation-based selection. It introduces an evaluation value that considers both the number of images per person and the quality/importance of each image, allowing for more nuanced and uniform representation while maintaining automated processing efficiency.
Solution Approach 2:
The patent replaces manual image selection with an automated evaluation system that calculates representation uniformity metrics and automatically determines which images to select or exclude, substituting mechanical human judgment with computational analysis.
2Manufacturing precision
If the number of images per person is strictly limited, then representation uniformity is improved, but the total number of selectable images decreases
Solution Approach 1:
The system applies different selection criteria to different individuals based on their specific representation status. Instead of a uniform limit applied to everyone, it evaluates each person's image count locally and determines selection/exclusion on an individual basis, allowing flexibility in maintaining overall uniformity while preserving sufficient image quantity.
3Measurement precision
If manual selection of images for deletion is required, then selection accuracy is improved, but the operation complexity and time consumption increase
Solution Approach 1:
The system performs self-service by automatically calculating representation uniformity, identifying images that should be excluded, and presenting recommendations to the user. This eliminates the need for manual analysis and selection, allowing users to simply review and confirm automated recommendations, thereby maintaining accuracy while simplifying operation.
4Extent of automation
If image selection is based on predetermined rules only, then automation level is improved, but the ability to handle complex representation scenarios deteriorates
Solution Approach 1:
The system introduces dynamic adjustment capabilities where the evaluation criteria and weights can be modified based on different scenarios and user preferences. The automation adapts to various representation scenarios by adjusting the evaluation function, maintaining high automation levels while improving versatility.
Data Source
AI summary
In a case where a plurality of images are input, images in which the same person is included are grouped. In a case where there are images of which the number is equal to or larger than a maximum number of images to be made public for the same person, a total image evaluation value is calculated for the images in which the same person is included. An image with a small total image evaluation value is determined as a private image candidate so that the number thereof is smaller than the maximum number.