Clothing Color and Pattern Matching With Bayesian Feedback
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Solution Overview
Problem
Existing methods for matching textiles, particularly articles of clothing, lack efficiency in identifying suitable pairs or outfits based on color and pattern analysis, and do not effectively incorporate user feedback to improve matching algorithms.
Innovation Solution
A method utilizing Bayesian probability analysis and user feedback to match articles of clothing by analyzing color and pattern information, creating a virtual wardrobe, and updating probabilities based on user feedback to enhance matching accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color and pattern matching methods are used, then the matching process is simple, but the accuracy and reliability of matching recommendations are insufficient
Solution Approach 1:
The patent implements feedback mechanisms where user responses to matching recommendations are collected and used to update the Bayesian probability model. The system continuously learns from user feedback, adjusting probability distributions to improve future matching accuracy. This creates a closed-loop system that progressively enhances measurement precision through iterative refinement.
Solution Approach 2:
The patent transforms the matching problem from simple color/pattern comparison to a probabilistic framework using Bayesian analysis. By changing the parameters from deterministic color values to probability distributions and likelihood ratios, the system achieves higher matching accuracy while managing complexity through mathematical formalization.
2Reliability
If Bayesian probability analysis is implemented, then matching accuracy improves, but computational time and processing complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating color and pattern features, storing them in structured formats before actual matching occurs. The Bayesian model parameters and probability distributions are prepared in advance, allowing faster computation during the actual matching process. This reduces real-time processing time while maintaining high reliability.
3Adaptability or versatility
If the system incorporates user feedback for iterative learning, then adaptability to user preferences improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where user responses to matching recommendations are collected and used to update the Bayesian probability model. The system continuously learns from user feedback, adjusting probability distributions to improve future matching accuracy. This creates a closed-loop system that progressively enhances measurement precision through iterative refinement.
Solution Approach 2:
The system performs self-service by automatically updating its own probability models and parameters based on collected user feedback without requiring manual reconfiguration. The Bayesian framework enables the system to self-adjust and adapt to changing user preferences autonomously, managing complexity through automated learning processes.
Data Source
AI summary
Textile matching using color and pattern recognition and methods of use are provided herein. An example method includes analyzing an image of a first article of clothing to obtain color information and pattern information, comparing the color information and pattern information of the first article of clothing to color information and pattern information for a plurality of other articles of clothing using Bayesian probability analysis to determine matched pairs, and providing a user with wardrobe suggestions using the matched pairs.