Multimedia Retrieval Scoring Function Optimization
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Solution Overview
Problem
Existing retrieval systems face challenges in selecting suitable similarity metrics for multimedia documents, leading to difficulties in constructing effective retrieval systems, especially when dealing with databases containing both text and image content, as they require a combination of multiple scoring metrics which is complex and inefficient.
Innovation Solution
The implementation of a document relevance scoring function that combines pseudo-relevance and cross-media relevance scoring components, optimized using a weighted linear combination, with training based on multimedia documents and queries, to generate a trained scoring function for improved retrieval operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple scoring metrics are combined for multimedia documents, then retrieval accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple scoring metrics (text relevance, image relevance, and cross-media relevance) into a unified scoring function that computes an overall relevance score for multimedia documents. This merging approach maintains high retrieval accuracy by considering multiple dimensions of relevance while presenting a single integrated scoring mechanism to users, thereby hiding the underlying complexity.
Solution Approach 2:
The unified scoring function serves multiple purposes: it evaluates text-only queries, image-only queries, and cross-media queries simultaneously. By designing a multi-functional scoring mechanism that can handle different query types and media combinations, the system achieves versatility without requiring separate complex systems for each query type.
2Measurement precision
If similarity metric-based retrieval is used, then retrieval precision is improved, but the number of retrieved documents cannot be constrained effectively
Solution Approach 1:
The system dynamically adjusts the number of retrieved documents based on the query type and user needs. For direct relevance queries, it retrieves a manageable number of highly relevant documents, while for pseudo-relevance queries, it can retrieve more documents to enable query expansion. The scoring function allows flexible control over retrieval quantity while maintaining precision through relevance-based ranking.
3Ease of manufacture
If keyword-based retrieval is used, then implementation simplicity is maintained, but retrieval accuracy deteriorates
Solution Approach 1:
The patent introduces a unified scoring function as an intermediary layer between the simple keyword matching and the final retrieval results. This scoring function takes multiple input factors (text similarity, image similarity, cross-media relevance) and combines them to produce an overall relevance score, thereby improving accuracy while maintaining a relatively simple implementation structure.
4Adaptability or versatility
If cross-media relevance querying is employed, then query expansion capability is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary scoring and ranking of candidate documents before full cross-media relevance computation. By first identifying potentially relevant documents using simpler metrics (such as text keyword matching or basic image similarity), the system reduces the search space for more computationally intensive cross-media relevance calculations, thereby managing computational complexity while maintaining query expansion capability.
Data Source
AI summary
In a retrieval application, a document relevance scoring function comprises a weighted combination of scoring components including at least one of a pseudo-relevance scoring component and a cross-media relevance scoring component. Weights of the document relevance scoring function are optimized to generate a trained document relevance scoring function. The optimizing is respective to a set of training documents including at least some multimedia training documents and a set of training queries and corresponding training document relevance annotations. A retrieval operation is performed for an input query respective to a database using the trained document relevance scoring function to retrieve one or more documents from the database.