Cross-Modal Search System Merging Content and Semantic Data Sets
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Solution Overview
Problem
Current search technologies lack support for cross-modal search, are limited to single search dimensions, suffer from low efficiency, and often provide incomplete search results.
Innovation Solution
A cross-modal search method and device that acquire first modality data, search in a second modality database based on content and semantic information, and merge the results to provide a comprehensive cross-modal search outcome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cross-modal search is implemented using multiple search dimensions (content and semantic information), then search comprehensiveness and diversity are improved, but system complexity increases
Solution Approach 1:
The search system is segmented into two independent search paths: one for content-based search and another for semantic-based search. Each path processes information separately and produces independent result sets, which are then merged. This segmentation allows the system to handle multiple search dimensions without creating complex interdependencies, resolving the contradiction between comprehensiveness and system complexity.
Solution Approach 2:
The search system is designed with multi-functionality to handle both content-based and semantic-based search operations within a single unified framework. The system can perform different types of searches (content matching and semantic matching) using the same infrastructure, improving versatility without proportionally increasing complexity.
2Loss of information
If dual search paths (content-based and semantic-based) are used, then search result completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing both content and semantic information separately before the actual search query is executed. This preliminary preparation allows the dual search paths to operate efficiently during query processing, reducing the time penalty that would otherwise result from performing both searches simultaneously without prior preparation.
Solution Approach 2:
The results from content-based search and semantic-based search are merged into a unified result set. This merging process consolidates the outputs from both search paths, ensuring completeness of search results while avoiding redundant processing of overlapping results, thereby mitigating the increase in processing time.
3Productivity
If multiple search dimensions are implemented, then search efficiency is improved through better matching, but computational resources increase
Solution Approach 1:
The search system applies local quality by using different matching strategies for different search dimensions: content-based search uses exact or near-exact matching for precision, while semantic-based search uses more flexible semantic similarity matching for broader coverage. This localized optimization of matching quality for each search type improves overall search efficiency without uniformly increasing computational resources across all operations.
Data Source
AI summary
A cross-modal search method includes: acquiring first modality data; searching in a second modality database based on content information of the first modality data to obtain a first set, the first set including at least one piece of second modality data matched with the content information of the first modality data; searching in the second modality database based on semantic information of the first modality data to obtain a second set, the second set including at least one piece of second modality data matched with the semantic information of the first modality data; and merging the first set and the second set to obtain a cross-modal search result corresponding to the first modality data.