De-duplication method for video material

By calculating the multi-level semantic tag matching degree and historical usage data of video clips, a weighted algorithm is used to select target clips, solving the problem of duplicate content in short video generation, realizing intelligent deduplication, and improving production efficiency and content uniqueness.

CN121722941AActive Publication Date: 2026-03-24LAINENG (HANGZHOU) E-COMMERCE CO LTD
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Patent Information

Application Number
CN202610226960.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-26
Publication Date
2026-03-24
Estimated Expiration
2046-02-26

AI Technical Summary

Technical Problem

Existing technologies rely on manual review after the video is generated to remove duplicate content, which is inefficient and produces inconsistent results, failing to meet the needs of large-scale production.

Method used

By acquiring multi-level semantic tag matching degree and historical usage data of script fragments and candidate video material fragments, tag matching degree, diversity score and cold start score are calculated, and a weighted algorithm is used to select target video material fragments to achieve intelligent deduplication.

Benefits of technology

By automatically avoiding duplicate content during the material matching stage, the automation level and content uniqueness of video production are improved, replacing inefficient manual review.

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Abstract

The invention relates to a video material deduplication method, which establishes an accurate range for subsequent processing by obtaining a script fragment and a semantic matched candidate video material fragment. The label matching degree is calculated based on the matching condition of the candidate video material segments and the scripts in the multi-level semantic label system, and it is ensured that the materials meet the script requirements in the subject and content categories. De-duplication related scores are calculated by analyzing the use history of candidate video material segments, the probability of repeatedly using the same or similar materials is effectively reduced through diversity scores, new materials are actively encouraged to be used through cold start scores, and the repetition risk and novelty of the materials are quantitatively evaluated from different dimensions. Finally, the target material is selected according to the tag matching degree and the weighting result of the deduplication correlation score, the content matching requirement and the deduplication target are organically combined, the repeated content is automatically avoided from the source in the material matching stage, a post-event manual auditing mode which is low in efficiency and high in subjectivity is replaced, and the intelligent level of video production is improved.
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