Visual Search Query Feature Extraction and Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional search systems face inefficiencies in quickly identifying and ranking visually similar content items in response to a visual search query, often relying on text annotations rather than visual features.
Innovation Solution
A visual discovery system that uses a neural network to extract and compare visual features from a visual search query with stored content items, employing an exact nearest neighbor algorithm and ranking mechanisms based on query, content, and context features to provide relevant and visually similar content items to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional search systems use text annotations to identify visual content, then the search process is simpler to implement, but the response time is longer and relevance is lower
Solution Approach 1:
The system pre-extracts visual features from content items and stores them in advance, so that when a visual search query is received, the comparison can be performed immediately without performing feature extraction during the search operation, thus reducing response time
Solution Approach 2:
The system replaces text annotation-based search with neural network-based visual feature extraction and comparison, substituting a simpler but less accurate method with a more complex but visually accurate method, improving relevance while managing complexity through efficient algorithms
2Measurement precision
If the system compares visual features of all content items, then the relevance of search results is improved, but the processing time and computational resources increase
Solution Approach 1:
The system segments the content database into different categories or groups, and when a visual search is performed, only compares the query with content items in relevant segments, reducing the number of comparisons needed while maintaining accuracy
Solution Approach 2:
The system performs partial comparison by first comparing visual features at a coarse level to identify candidate matches, then performs more detailed comparison only on those candidates, avoiding full comparison with all content items while maintaining precision
Data Source
AI summary
A system for visual discovery is disclosed. The system identifies a visual search query in response to an action associated with an image presented to a user of a client device. The system extracts visual features from the visual search query and compares the visual features with visual features of stored content items. The system then identifies a set of candidate visual content items from the stored content items that have visual features, which are similar to the visual features of the visual search query. The candidate visual content items are ranked using information from a user session and provided for display to the user.