AI Multimedia Image Ranking for Infant Monitoring Selection
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Solution Overview
Problem
Conventional AI recommendation systems for selecting multimedia images in infant monitoring systems fail to meet user expectations due to inconsistent weighting of selection conditions such as facial clarity, body proportion, and interaction between objects, leading to suboptimal image selection.
Innovation Solution
A processing method and electronic device that utilize artificial intelligence to assign weighted scores based on target detection, interaction analysis, position assessment, and facial confirmation to rank multimedia images, ensuring clear and centered images of infants with interactive content are prioritized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AI recommendation systems select multimedia images without weighted selection conditions, then the selection process is simple, but the selected images fail to meet user expectations
Solution Approach 1:
The patent applies parameter changes by introducing weighted scoring parameters for multiple selection conditions (facial clarity weight W1, body proportion weight W2, interaction weight W3, position weight W4). Each condition is evaluated and assigned a score that is multiplied by its corresponding weight to generate a comprehensive evaluation result, transforming the image selection from a simple process to a precision-based weighted decision system
Solution Approach 2:
The patent segments the image selection process into four independent evaluation modules: facial clarity assessment, body proportion assessment, interaction detection, and position evaluation. Each module independently evaluates its specific condition and generates a separate score, which are then combined through weighted summation to produce the final selection result, enabling precise control over each selection criterion
2Ease of operation
If multiple selection conditions are evaluated with equal weight, then the evaluation process is straightforward, but the results do not prioritize user-preferred image qualities
Solution Approach 1:
The patent changes the parameter weights to reflect user preferences, assigning different weights to different selection conditions (W1 for facial clarity, W2 for body proportion, W3 for interaction, W4 for position). This allows the system to prioritize qualities that users value most while maintaining a straightforward weighted summation evaluation process that combines multiple conditions efficiently
Solution Approach 2:
The patent applies local quality by assigning different weights to different aspects of image quality based on their importance to user satisfaction. Rather than treating all conditions equally, the system gives higher weight to locally important qualities (such as facial clarity for infant monitoring) while still considering other factors, creating a nuanced evaluation that balances simplicity with reliability
3Measurement precision
If AI analysis performs comprehensive image assessment, then image quality is improved, but processing time increases
Solution Approach 1:
The patent segments the comprehensive AI analysis into four parallel evaluation streams that can be processed simultaneously: facial clarity detection, body proportion analysis, interaction detection, and position assessment. By dividing the assessment into independent modules that operate in parallel rather than sequentially, the system maintains high measurement precision while reducing overall processing time
Solution Approach 2:
The patent applies preliminary action by performing basic image preprocessing and feature extraction before the detailed weighted evaluation. The system pre-identifies key features (faces, bodies, positions) and prepares evaluation data in advance, so that the actual scoring and weighting process can proceed efficiently without redundant processing steps
Data Source
AI summary
A processing method for selecting a multimedia image based on a ranking performed by an electronic device includes reading an executable code, identifying multiple multimedia images by using artificial intelligence, and performing rating and sorting processing on the multiple multimedia images. The method includes the steps of target detection, interaction analysis, position assessment, facial confirmation, and ranking. The method obtains a total grading score for each multimedia image by summing according to a target confirmation grading score, an interaction grading score, a position grading score, and a facial confirmation grading score, and selects a user-desired multimedia image according to a ranking of the total grading score. An electronic device for selecting a multimedia image based on a ranking and a terminal device communicatively connected thereto, and a non-transitory computer-readable recording medium are also provided.