Unified Image Search System Combining Semantic and Visual Attributes
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Solution Overview
Problem
Current image search systems rely heavily on text-based metadata and tagging, which can be laborious and expensive, and do not effectively utilize image content for retrieval, limiting the accuracy and efficiency of searches.
Innovation Solution
Integration of semantic description and visual attribute search functionalities into a unified image search system, allowing users to combine semantic image description constraints with visual attribute constraints, using a scene category classifier and visual similarity module to rank and group images based on content similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based metadata and tagging are used for image search, then the search system can operate with existing infrastructure, but the search accuracy and efficiency are limited
Solution Approach 1:
The patent combines text-based metadata search with content-based visual feature search into a unified search system. The search engine integrates both textual keywords and visual attributes (color, shape, texture) to retrieve images, allowing users to search using either text or visual queries, thereby improving search accuracy without requiring complete system redesign
Solution Approach 2:
The search system is designed to accept multiple types of queries (text-based and visual-based) and process them through a unified architecture. The system can handle both traditional keyword searches and content-based searches using visual features, making the search infrastructure multi-functional and adaptable to different user needs
2Measurement precision
If content-based image retrieval analyzes actual image content, then search accuracy improves, but the computational cost and time required increase
Solution Approach 1:
The system pre-processes images by extracting and storing visual features (color histograms, shape descriptors, texture patterns) before actual search operations. This preliminary feature extraction creates an indexed representation of image content that can be quickly queried during search, reducing real-time computational requirements while maintaining high search accuracy
Solution Approach 2:
The patent transforms continuous visual data into discrete parameter representations (quantized color histograms, normalized shape descriptors). By converting complex continuous image data into simplified parameter sets, the system reduces computational complexity during search while preserving the essential visual characteristics needed for accurate retrieval
3Adaptability or versatility
If visual attribute search is added to text-based search, then search functionality is enhanced, but system complexity increases
Solution Approach 1:
The search system is segmented into independent modules: text-based search module, visual feature extraction module, and unified search ranking module. Each module processes specific types of data (text or visual) and contributes to the final search results through a standardized interface, allowing the system to handle multiple search types without creating a monolithic complex structure
Data Source
AI summary
An image search method includes receiving a first query, the first query providing a first image constraint. A first search of a plurality of images is performed, responsive to the first query, to identify a first set of images satisfying the first constraint. A first search result, which includes the first set of images identified as satisfying the first constraint, is presented. A second query is received, the second query providing a second image constraint with reference to a first image of the first set of images. A second search of the plurality of images is performed, responsive to the second query, to identify a second set of images that satisfy the second constraint. A second search result, which includes the second set of images identified as satisfying the second constraint, is presented.