Fashion Attribute Analysis Using AI Image Classification

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Solution Overview

Problem

Current methods are inefficient and costly for analyzing and classifying fashion products using large amounts of image data, requiring significant time and resources to categorize and analyze products by various attributes.

Innovation Solution

A method and system utilizing an AI model for statistical analysis and visualization of fashion attributes, including collecting image data, performing attribute classification, and providing graphical representations of the analysis results, to efficiently analyze and categorize fashion items based on attributes like category, color, material, and style.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional manual methods are used to classify and analyze product information by category and attributes, then analysis accuracy can be maintained, but it requires significant time and resources making it inefficient for large amounts of image data

Engineering Contradiction:

Improveanalysis efficiencyVSAvoidtime required for classification

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical classification processes with an automated image recognition system using deep learning models. The system automatically detects fashion items in images, extracts attributes such as category, color, material, and style, and performs statistical analysis without human intervention, thereby dramatically improving productivity while reducing time loss.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If individual product information is input and analyzed one by one, then detailed attribute analysis is achieved, but it becomes costly and time-consuming for large datasets

Engineering Contradiction:

Improveattribute analysis precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent merges multiple individual product analysis operations into a single batch processing system. The image recognition model processes multiple images simultaneously, extracting and analyzing attributes for numerous products in parallel rather than sequentially, which maintains measurement precision while reducing overall system complexity and resource requirements.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal image recognition system that can handle multiple fashion items across different categories, attributes, and image types through a single multi-functional model. This universal system performs detection, attribute extraction, and statistical analysis for diverse fashion products without requiring separate specialized systems for each product type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20240037912A1Method, system and non-transitory computer-readable recording medium for analyzing fashion attributes of image data group using large image data

Publication Date: 2024.02.01 MUSINSA CO LTD

AI summary

A method for analyzing fashion attributes using large amounts of pieces of image data is provided. The method includes: collecting image data including at least one item; statistically analyzing on the image data based on an attribute classification AI model; and visualizing the results of the statistical analysis, wherein the attribute classification AI model is a model for detecting said at least one item included in the image data, and for recognizing, labeling and classifying fashion attributes of the at least one item.