Multi-Image Retrieval System Clustering and Metadata Correlation
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Solution Overview
Problem
Current image search technologies often return misleading or irrelevant results, failing to provide comprehensive and chronologically ordered images that meet user intent, and lack correlation between images and content, especially when searching across different search engines and databases.
Innovation Solution
A multi-image information system that performs image searching by determining user intent and correlating images with related information, using metadata and content correlation managers to provide personalized and relevant results, including workflow solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image search strategies focus on individual image files with minimal matching, then search implementation is simplified, but search result accuracy and relevance deteriorate
Solution Approach 1:
The patent combines multiple images into clusters based on visual similarity and metadata correlations. Instead of searching individual images in isolation, the system groups related images together and searches the entire cluster, thereby improving search accuracy while maintaining implementation feasibility through automated clustering algorithms.
Solution Approach 2:
The patent introduces metadata as an intermediary layer between images and search queries. Metadata tags, descriptions, and contextual information serve as mediators that enhance the matching between search requests and relevant images, significantly improving search result accuracy without complicating the core search mechanism.
2Productivity
If image search returns generic results with minimal information, then search processing speed is improved, but information completeness and user intent satisfaction deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-processing images during ingestion - extracting metadata, generating descriptions, creating clusters, and organizing hierarchical relationships. This preliminary processing enables the system to return comprehensive, information-rich results quickly during actual search operations, as the heavy lifting has already been done in advance.
Solution Approach 2:
The patent implements dynamic result presentation that adapts to user needs. The system can return varying levels of detail based on query context, user preferences, and result relevance. Search results dynamically include correlated images, metadata, descriptions, and workflow information, balancing processing speed with information completeness.
3Ease of manufacture
If image search strategies are specific to particular search engines and interfaces, then implementation is simpler, but system adaptability and versatility deteriorate
Solution Approach 1:
The patent implements a universal image search architecture that can operate across multiple search engines, platforms, and interfaces. The core clustering and metadata-based search mechanisms are platform-agnostic, allowing the same system to be deployed on different search engines and integrated with various user interfaces without requiring engine-specific implementations.
4Device complexity
If image search clusters images based on minimum string matching, then computational complexity is reduced, but correlation accuracy between images and content deteriorates
Solution Approach 1:
The patent replaces simple string-matching mechanisms with more sophisticated correlation methods. Instead of relying solely on text overlap, the system uses visual similarity analysis, metadata matching, and contextual relationships to cluster images and associate them with relevant content, significantly improving correlation accuracy while managing computational complexity through efficient algorithms.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for improved image search and retrieval. In various embodiments, a multi-image information retrieval system is implemented to perform image searching and provide image search results based on user intent. Returned image results include correlated images and associated information regarding objects such as product lines. Images can include metatags and are updated with correlated information.