Visual Item Identification with Pre-Trained Feature Encodings
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Solution Overview
Problem
Conventional item identification systems require extensive retraining and high computational resources to recognize new items, leading to inefficiencies and increased processing power consumption.
Innovation Solution
A method and system that utilize pre-trained classifiers to determine item encodings from visual information, allowing for rapid identification of items without retraining, using a combination of item and shape classifiers to generate feature vectors for accurate recognition, and a comparison module to match these vectors with known items, reducing memory and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional item identification systems are used to recognize new items, then identification accuracy is maintained, but extensive retraining and high computational resources are required
Solution Approach 1:
The system performs preliminary encoding of items during a training phase, storing encoded representations in a database. When identification is needed, the pre-encoded items are quickly compared against new inputs without requiring retraining, thus reducing real-time computational resources while maintaining accuracy
Solution Approach 2:
The patent extracts essential visual features and encodes them into compact representations, separating the complex visual processing from the identification task. This extraction allows rapid comparison and matching while reducing the computational burden during actual identification operations
2Adaptability or versatility
If conventional item identification systems are used to recognize new items, then comprehensive item recognition is achieved, but system complexity and retraining requirements increase
Solution Approach 1:
The system creates encoded copies of item representations and stores them in a database. These encoded copies serve as templates for rapid comparison with new items, enabling the system to adapt to new items simply by adding their encoded representations without modifying or retraining the core identification model
Solution Approach 2:
The patent transforms visual information into encoded representations with specific dimensional parameters. By changing the representation format from raw images to structured encodings, the system enables flexible addition of new items through parameter insertion rather than model retraining, reducing system complexity
Data Source
AI summary
The method for item identification preferably includes determining visual information for an item; calculating a first encoding using the visual information; calculating a second encoding using the first encoding; determining an item identifier for the item using the second encoding; optionally presenting information associated with the item to a user; and optionally registering a new item.