Personalized Visual Media Search via Semantic Analysis
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Solution Overview
Problem
Existing visual media search solutions cannot meet the demand for personalized queries as they fail to identify user-specific information, limiting their ability to recognize personalized relationships and events in images, such as searching for 'daughter's birthday photos' due to privacy concerns during model training.
Innovation Solution
A method and device for personalized search of visual media that conducts semantic analysis on user queries to extract visual, time, and location information, and performs semantic similarity matching with attribute data containing personalized information to generate relevant search results, incorporating user-specific tags and associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic analysis is performed on user queries to extract visual, time, and location information, then search accuracy for personalized content is improved, but system complexity increases due to multiple analysis modules
Solution Approach 1:
The system segments the search process into distinct functional modules: a semantic analysis module that extracts visual, time, and location information from queries, and a matching module that compares extracted information with media attributes. This segmentation allows each module to specialize in specific tasks, improving overall search accuracy while maintaining manageable system complexity through clear modular boundaries.
Solution Approach 2:
The semantic analysis module serves multiple functions by simultaneously extracting visual semantic information, time information, and location information from user queries. This multi-functionality improves search accuracy for personalized content without requiring separate analysis modules for each information type, thereby controlling system complexity.
2Adaptability or versatility
If user-specific information is utilized for personalized search, then search relevance is improved, but user privacy protection is compromised
Solution Approach 1:
The system applies local quality by processing and storing user-specific information locally on the user's device rather than transmitting it to remote servers. The semantic analysis module extracts personalized information from local queries, and the matching module compares this information with locally stored media attributes. This localized processing enables search personalization while protecting user privacy by keeping sensitive data on-device.
3Adaptability or versatility
If semantic similarity matching is performed on attribute data containing personalized information, then personalized search capability is improved, but computational resources increase
Solution Approach 1:
The system extracts only the essential visual, time, and location information from user queries using the semantic analysis module, rather than processing entire queries. This extraction approach enables personalized search capability by comparing extracted key information with media attributes, while reducing computational resource consumption by avoiding unnecessary processing of redundant query components.
Data Source
AI summary
The application discloses a method and device for personalized search of visual media. Semantic analysis is conducted on a visual media query text of a user to obtain visual semantic information, time information and/or location information. Semantic similarity matching is conducted on a result of the semantic analysis and attribute data of each visual medium within a specified search range to obtain a query similarity of the visual medium. The visual medium is an image or a video, and the attribute data include personalized visual semantic information, personalized time information and/or personalized location information. A corresponding visual media query result is generated based on the query similarity. By adopting the application, users are provided with visual media which is a result of a personalized.