Automated Image Identification via Data Model Parameter Selection
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Solution Overview
Problem
Existing image recognition methods require manual selection of multiple determination conditions for comparing images, leading to inconsistent results and increased maintenance costs, as they are sensitive to fluctuations in pairwise comparison determination results.
Innovation Solution
A process and system for image identification that acquires images, extracts target characteristic information, and determines whether they belong to the same object using a trained data model, which includes data sets from images of the same and different objects, to reduce inconsistencies in image determination results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual selection of multiple determination conditions is used for image comparison, then the system can handle complex image identification tasks, but the determination results become inconsistent and maintenance costs increase
Solution Approach 1:
The patent transforms the determination conditions from manual selection to automated parameter-based selection. A data model is constructed with multiple parameters including similarity thresholds, image quality metrics, and comparison weights. The system automatically adjusts these parameters based on image characteristics, eliminating manual intervention and ensuring consistent determination results while maintaining adaptability to different image types.
Solution Approach 2:
The patent introduces a data model as an intermediary between image input and determination output. This data model serves as a mediator that standardizes the comparison process by automatically selecting and weighting determination conditions based on pre-defined criteria. The intermediary layer ensures that all image comparisons follow the same systematic approach, improving consistency while handling diverse image identification tasks.
2Measurement precision
If multiple pairwise comparison determination conditions are selected manually, then comprehensive image analysis can be performed, but the process requires large amounts of manual or semi-automated support
Solution Approach 1:
The patent enables the system to automatically select and configure determination conditions without manual intervention. The data model self-adjusts by evaluating image characteristics and automatically determining the appropriate comparison parameters, thresholds, and weights. This self-service mechanism eliminates the need for manual or semi-automated support while maintaining comprehensive image analysis capabilities through systematic automated decision-making.
Solution Approach 2:
The patent performs preliminary configuration of determination conditions during the data model construction phase. Pre-defined rules and criteria are established in advance to guide automatic condition selection. This preliminary action ensures that when image comparisons are performed, the system can immediately apply appropriate determination conditions without requiring manual setup, thereby reducing manual support requirements while maintaining analysis comprehensiveness.
3Quantity of substance
If N(N-1)/2 pairwise comparison determination conditions are manually selected for N images, then all possible image pairs can be compared, but the scope definition becomes complex and maintenance costs increase
Solution Approach 1:
The patent segments the overall comparison task into manageable units by organizing images into groups based on preliminary analysis. Instead of requiring all N(N-1)/2 pairwise comparisons, the system divides images into subsets that need comparison based on similarity pre-assessment, temporal relationships, or source groupings. This segmentation reduces the number of required comparisons while maintaining comprehensive analysis, and simplifies scope definition by providing a structured approach to determining which comparisons are necessary.
4Reliability
If multiple determination conditions are used for image comparison, then thorough verification can be achieved, but service personnel cannot directly grasp whether images are of the same person
Solution Approach 1:
The patent extracts the complex multi-condition determination process into a separate automated data model that operates independently from the user interface. Service personnel interact with a simplified interface that displays only the final determination result and key confidence metrics. The complex verification logic, involving multiple determination conditions and thresholds, is encapsulated within the automated data model, allowing thorough verification to occur behind the scenes while presenting only essential information to users for easy visual graspability.
Data Source
AI summary
Image identification is disclosed including acquiring N images, N being a natural number greater than 1, extracting target characteristic information from respective ones of the N images to obtain a first data set corresponding to the N images, and determining, using a data model, a category associated with the first data set corresponding to the N images.