Clothing Recognition System for Style Matching
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Solution Overview
Problem
Current online fashion recommendation systems have limited ability to suggest items that align with an individual's personal sense of fashion, as they fail to capture the complex and context-dependent nature of fashion preferences, which are influenced by factors like color, texture, collar configuration, sleeve length, and demographic information.
Innovation Solution
A clothes recognition system that captures images of clothing items, determines key features such as color composition, texture, collar, and sleeve configuration, and uses machine learning to classify and match clothing items based on these features, providing recommendations that align with the user's style by analyzing images in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collaborative filtering and content similarity techniques are used for fashion recommendation, then item recommendations can be provided, but the ability to capture individual fashion perception and context-dependent style preferences is limited
Solution Approach 1:
The patent segments the fashion recommendation problem into multiple independent analysis components: color composition analysis, texture composition analysis, collar configuration detection, sleeve configuration detection, and pattern recognition. Each component processes specific visual features separately and contributes to the overall style matching, enabling comprehensive capture of individual fashion preferences without losing nuanced perceptual information
Solution Approach 2:
The patent transitions from traditional collaborative filtering (user-based dimension) and content similarity (attribute-based dimension) to a visual feature-based dimension. By analyzing actual visual characteristics of clothing items through image processing and comparing them with user-provided reference images, the system creates a new dimension of recommendation that directly captures individual fashion perception and context-dependent style preferences
2Measurement precision
If multiple clothing features (color, texture, collar, sleeve, pattern) are analyzed, then fashion style matching accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex task of style matching into separate modular analysis functions: color composition analysis, texture composition analysis, collar configuration detection, sleeve configuration detection, and pattern recognition. Each module processes a specific feature independently, making the overall system more manageable and easier to implement while achieving high measurement precision through comprehensive feature analysis
Solution Approach 2:
The patent creates a multi-functional clothing analysis system where a single integrated platform performs multiple analysis tasks (color, texture, collar, sleeve, pattern) on clothing images. This universal system handles diverse fashion attributes through a unified approach, reducing the need for separate specialized systems for each feature and thereby managing complexity while maintaining high accuracy
Data Source
AI summary
One embodiment of the present invention provides a system for recognizing and classifying clothes. During operation, the system captures at least one image of a clothing item. The system further determines a region on the captured image which corresponds to a torso and/or limbs. The system also determines at least one color composition, texture composition, collar configuration, and sleeve configuration of the clothing item. Additionally, the system classifies the clothing item into at least one category based on the determined color composition, texture composition, collar configuration, and sleeve configuration. The system then produces a result which indicates the classification.