Wearable Item Image Analysis via Feature Extraction
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Solution Overview
Problem
Conventional methods for image processing and object recognition in wearable item analysis, such as neural network approaches, require significant computational resources and data, and lack transparency in their decision-making processes, making them costly and difficult to explain.
Innovation Solution
A system and method for analyzing images of wearable items that avoids training neural networks by identifying patterns and colors directly from images, allowing for transparent and efficient analysis without the need for extensive data and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If neural network approaches are used for image processing and object recognition, then object recognition capability is improved, but computational resources and costs increase significantly
Solution Approach 1:
The patent segments the image processing task into distinct stages: initial image filtering to identify potential wearable items, extraction of key visual features (colors, patterns, shapes), and matching against inventory database. This segmentation avoids the need for comprehensive neural network processing of entire images, reducing computational load while maintaining recognition accuracy.
Solution Approach 2:
The patent extracts only the essential visual features (colors, patterns, shapes) from images rather than processing complete images through neural networks. This extraction approach captures sufficient information for wearable item identification while dramatically reducing the data volume and computational resources required for analysis.
2Measurement precision
If neural network approaches are used for wearable item analysis, then recognition accuracy is improved, but transparency and explainability deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between image input and recognition output: explicit extraction and display of visual features (colors, patterns, shapes). This intermediary makes the recognition process transparent by showing which specific visual characteristics led to the identification result, while still achieving accurate wearable item recognition through systematic feature analysis.
3Reliability
If extensive data is used for training neural networks, then model performance is improved, but data requirements and storage needs increase
Solution Approach 1:
The patent extracts only the necessary visual features (colors, patterns, shapes) from images for analysis and matching, rather than relying on large volumes of training data to teach neural networks. This extraction approach achieves reliable wearable item identification with minimal data requirements, as the system directly analyzes extracted features against inventory rather than requiring extensive trained models.
Data Source
AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.