Multimedia Search Vector Clustering for Reduced Latency
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Solution Overview
Problem
Current multimedia search methods are inefficient due to brute force searches in a global space, leading to long search times and low efficiency.
Innovation Solution
The method involves acquiring a query vector, determining a matching category in a search library using clustering algorithms, and inquiring for a target vector within that category to retrieve multimedia content, employing techniques like Approximate Nearest Neighbor (ANN) and vector quantization to improve search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If brute force search is performed in global space, then search completeness is maintained, but search time increases significantly
Solution Approach 1:
The patent segments the global search space into multiple categories based on clustering algorithms. Instead of searching the entire global space, the system first determines which category matches the query vector and then performs similarity calculation only within that specific category. This segmentation dramatically reduces the search scope and time while maintaining search effectiveness.
Solution Approach 2:
The patent performs preliminary category determination before the actual similarity calculation. By pre-clustering multimedia contents into categories and pre-calculating category representatives, the system prepares the search structure in advance. This preliminary action enables faster search execution by avoiding the need to process all multimedia contents from scratch.
2Device complexity
If word segmentation and feature extraction are performed on all multimedia contents, then search accuracy is maintained, but calculation burden increases
Solution Approach 1:
The patent extracts and pre-calculates category representative features for each category during the indexing phase. These category representatives capture the essential characteristics of each category without requiring processing of all individual multimedia contents during search. This extraction allows the system to maintain search accuracy while significantly reducing the calculation burden during actual search operations.
Solution Approach 2:
The patent changes the search parameter from individual multimedia content features to category-level representative features. Instead of calculating similarities between query vectors and all individual contents, the system compares query vectors with category representatives first, then performs refined search within the matching category. This parameter change reduces computational complexity while preserving search accuracy.
Data Source
AI summary
The present application relates to a field of smart searching technology, and provides a method and an apparatus for searching a multimedia content, a device, and a storage medium. The method includes: acquiring a query vector of query information; determining, from a search library, a first category matching the query vector, wherein the search library comprises a plurality of categories, each of the categories comprises a plurality of vectors, and each of the vectors is associated with a corresponding multimedia content; and inquiring, in the first category, a target vector matching the query vector, and acquiring the multimedia content corresponding to the target vector.