3D Feature Extraction from 2D Images via Neural Networks
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Solution Overview
Problem
Conventional search methods in electronic marketplaces rely on keyword-based queries, which struggle to efficiently find items with specific visual attributes, especially three-dimensional features, leading to frustration for users seeking precise matches.
Innovation Solution
The system allows users to capture images of items and uses a trained neural network to extract three-dimensional features, comparing them to a database of items to return visually similar products, enabling precise searches beyond traditional keyword limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search queries are used, then the search system is simple to operate, but the search precision for visual attributes is poor
Solution Approach 1:
The patent replaces the mechanical keyword-matching system with an image-based neural network recognition system. Users capture images of items instead of typing keywords, and the neural network automatically extracts visual features and matches them with database items, fundamentally substituting the search mechanism from text-based to vision-based processing
Solution Approach 2:
The patent introduces an intermediary neural network system that bridges the gap between user intent and database search. The neural network acts as a mediator that translates captured images into meaningful visual feature comparisons, enabling precise matching without requiring users to understand complex search query formulations
2Reliability
If conventional object recognition services are used, then the system architecture is simple, but the object recognition speed and accuracy are insufficient
Solution Approach 1:
The patent performs preliminary actions by pre-processing and extracting visual features from captured images before the actual search operation. The neural network prepares feature representations in advance, which are then efficiently compared against the database, reducing the time required during the critical search moment
Solution Approach 2:
The patent segments the object recognition process into distinct stages: image capture, neural network feature extraction, feature comparison, and result retrieval. This segmentation allows each stage to be optimized independently, improving overall recognition speed and accuracy while maintaining system manageability
Data Source
AI summary
Various approaches provide for visual similarity-based search techniques that allow a user to provide two-dimensional image data (e.g., still images or video) about an item and search for items having related three-dimensional features. In order to create an electronic catalog of items that is searchable by three-dimensional features, the features are extracted from image data for each item. The three-dimensional features can be extracted using a trained model, such as a trained neural network or other machine learning-based approach. Thus, three-dimensional features of items in the electronic catalog can be determined and used to select or rank the items in response to a visual search query based on determined three-dimensional features of the query item. For example, three-dimensional features of the query item can be used to query the electronic catalog of items, in which items having visual attributes of similar three-dimensional features are selected and returned as search results.