Image Extraction Reference Determination Using Nested Group Analysis
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Solution Overview
Problem
Existing image extraction technologies fail to accurately identify and prioritize images of high importance within a group, especially when the image group hierarchy is considered, leading to missed important images in lower-level groups due to focus on higher-level group relationships.
Innovation Solution
An image extraction device and method that selects a second image group from a first image group, analyzes relationships within both groups, and determines an extraction reference based on the higher-level group's image information to prioritize images in the second group, ensuring important images are extracted accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image extraction focuses only on the second image group (lower-level group), then extraction speed is improved, but important images are missed due to lack of context from higher-level groups
Solution Approach 1:
The system performs preliminary analysis on the first image group (higher-level group) to establish relationship information between persons before extracting images from the second image group. This preliminary action ensures that important images are not missed during the extraction process from the lower-level group, as the relationship context is already available.
Solution Approach 2:
The patent implements a nested structure where the second image group (lower-level) is analyzed within the context of the first image group (higher-level). The extraction reference determination unit combines relationship information from both levels, nesting the lower-level analysis within the higher-level framework to achieve both speed and accuracy.
2Measurement precision
If image extraction considers relationship information from both first and second image groups, then importance identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the image extraction process into two distinct phases: first analyzing the higher-level image group to establish relationship information, then using that information to guide extraction from the lower-level image group. This segmentation allows the system to process information in manageable chunks, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The system performs relationship analysis partially - only on the first image group - rather than redundantly analyzing both groups. This partial action approach provides sufficient relationship context for accurate extraction from the second group without the excessive processing time that would result from analyzing both groups in full detail.
3Device complexity
If the extraction reference is determined based only on the second image group, then device complexity is reduced, but extraction accuracy deteriorates
Solution Approach 1:
The extraction reference determination unit is designed to universally handle relationship information from multiple image groups. It can process and integrate relationship data from both the first and second image groups, making the system multi-functional in terms of information sources while maintaining a single, unified extraction decision-making process.
Data Source
AI summary
In the image extraction device, an instruction acquisition unit acquires an instruction input by a user, and an image group selection unit selects a second image group, which has a smaller number of images than a first image group, from the first image group in response to the instruction. Then, an extraction reference determination unit determines an image extraction reference when extracting an image from the second image group based on images included in the first image group, and an image extraction unit extracts one or more images, the number of which is smaller than the number of images in the second image group, from the second image group according to the image extraction reference.