Video Search Duplicate Removal via Digital Signatures
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Solution Overview
Problem
Search engines face difficulties in effectively classifying and distinguishing multimedia content, particularly video content, due to the lack of text metadata, leading to the inclusion of duplicate results in search queries, which decreases user satisfaction and the effectiveness of video searches.
Innovation Solution
The implementation of digital signatures for video content items, using mathematical functions to generate unique identifiers, allows for near-duplicate detection and removal, improving search results by flagging and removing duplicate video content items based on similarity thresholds, while utilizing secondary signatures to detect more duplicates with increased sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital signature-based duplicate detection is implemented, then duplicate video content is removed improving result uniqueness, but computational complexity increases
Solution Approach 1:
The duplicate detection process is divided into multiple stages: first generating digital signatures for video content, then comparing signatures to identify duplicates, and finally removing detected duplicates. This segmentation allows the system to handle the complex task in manageable steps, reducing overall computational complexity while maintaining detection accuracy.
Solution Approach 2:
Digital signatures are generated and stored in advance for video content items before actual search queries are processed. This preliminary action enables rapid duplicate detection during search operations, as the system only needs to compare query results against pre-computed signatures rather than performing full video analysis in real-time.
2Reliability
If comprehensive duplicate detection is performed on all video results, then result quality improves, but processing time increases
Solution Approach 1:
The system applies duplicate detection selectively rather than uniformly to all video results. By focusing detection efforts on cases where digital signatures indicate potential duplicates, the system achieves high result quality while avoiding unnecessary processing time for clearly unique content.
Solution Approach 2:
The patent replaces traditional mechanical video comparison methods with digital signature-based detection. Instead of computationally intensive frame-by-frame analysis, the system uses mathematical hash functions to generate signatures and compare them, dramatically reducing processing time while maintaining reliable duplicate identification.
3Adaptability or versatility
If digital signatures are used for video identification, then duplicate detection capability is enhanced, but system complexity increases
Solution Approach 1:
The digital signature system serves multiple functions: it identifies video content, detects duplicates, and enables rapid comparison across different search queries. This universal approach replaces multiple specialized detection mechanisms, enhancing versatility while actually reducing overall system complexity through consolidation.
Solution Approach 2:
Digital signatures act as an intermediary representation between the original video content and the duplicate detection process. Instead of directly comparing video files, the system compares their signature representations, which simplifies the detection mechanism while maintaining accurate duplicate identification capability.
Data Source
AI summary
Duplicate video search results are detected and removed. Digital signatures are generated for each video content item of a video content corpus. Duplicates are determined for the top n previously received queries by determining the similarity of video content items that are within the same results set of each particular query of the top n previously received queries. Similarities are calculated between any two video documents of the result set of the particular query by measuring the difference between the digital signatures of two video documents. If a similarity between two videos is determined to be above a particular threshold, then the two videos are considered duplicates of each other and the search index is updated by retaining the most relevant of the video documents to the particular query. The less relevant video documents are flagged as duplicates with respect to the particular query.