A video material deduplication method

Abstract

The application relates to a video material deduplication method, which establishes an accurate range for subsequent processing by acquiring a script segment and a candidate video material segment matched in semantics. A label matching degree is calculated based on the matching of the candidate video material segment and the script in a multi-level semantic label system, so that the material meets the script requirements in the theme and content category. A deduplication-related score is calculated by analyzing the use history of the candidate video material segment, wherein the diversity score effectively reduces the probability of using the same or similar material repeatedly, and the cold start score actively encourages the use of new material, and the repetition risk and novelty of the material are quantitatively evaluated from different dimensions. Finally, the target material is selected according to the weighted results of the label matching degree and the deduplication-related score, and the content matching requirement and the deduplication target are organically combined, so that the repetition content is automatically avoided from the source in the material matching stage, and the inefficient and subjective post-artificial review mode is replaced, and the intelligent level of video production is improved.

Claims

1. A method for deduplicating video footage, characterized in that, include: Retrieve script fragments and multiple candidate video clips that match their semantics; Based on the matching of the candidate video clips and script clips in the preset multi-level semantic tagging system, the tag matching degree is calculated; Based on the usage history of the candidate video clips, a deduplication-related score is calculated; the deduplication-related score includes at least a diversity score for reducing the repeated use of the same video clip or the use of highly similar video clips, and a cold start score for encouraging the use of new video clips. Based on the weighted result of the tag matching degree and deduplication correlation score, one or more target video clips are selected from the plurality of candidate video clips; The step of selecting one or more target video clips from the plurality of candidate video clips based on the weighted result of the tag matching degree and deduplication correlation score further includes: Set a scoring threshold to filter out candidate video clips whose weighted results are below the scoring threshold; Select one or more of the highest weighted video clips from the remaining candidate video clips as the target video clips; After selecting one or more target video clips from the plurality of candidate video clips based on the weighted result of the tag matching degree and deduplication correlation score, the method further includes: Iterate through all target video clips and determine whether the ratio of the number of target video clips that completely match the script clips in the multi-level semantic tag system to the total number of target video clips is greater than or equal to a preset ratio. If the ratio of the number of target video clips that completely match the script clips in the multi-level semantic tag system to the total number of target video clips is less than a preset ratio, then candidate video clips that completely match the script clips in the multi-level semantic tag system are selected from candidate video clips whose weighted results are lower than the scoring threshold, to replace all non-completely matching clips in all target video clips, until the ratio is greater than or equal to the preset ratio.

2. The method for deduplicating video materials according to claim 1, characterized in that, The calculation of the deduplication-related score based on the usage history of the candidate video clips includes: The historical usage frequency of candidate video clips within a preset time period is counted; the higher the historical usage frequency, the lower the diversity score. Calculate the similarity between candidate video clips and the selected set of clips; A diversity score is calculated based on the historical usage frequency and the similarity; the higher the historical usage frequency, the lower the diversity score; the higher the similarity, the lower the diversity score.

3. The method for deduplicating video materials according to claim 1, characterized in that, The calculation of the deduplication-related score based on the usage history of the candidate video clips includes: The cold start score is calculated based on the historical usage frequency of candidate video clips; the lower the historical usage frequency, the higher the cold start score; and the candidate video clips that have never been used are assigned the highest cold start score.

4. The method for deduplicating video materials according to claim 1, characterized in that, The weighted result also incorporates semantic similarity. The selection of one or more target video clips from the plurality of candidate video clips based on the weighted result of the tag matching degree and deduplication relevance score includes: Convert the text of the script fragment and the text of the candidate video clip into feature vectors respectively; Calculate the similarity between two feature vectors and use the similarity between the two feature vectors as the semantic similarity score.

5. The method for deduplicating video materials according to claim 1, characterized in that, The weighted result also incorporates a coherence score. The selection of one or more target video clips from the plurality of candidate video clips based on the weighted result of the tag matching degree and deduplication correlation score includes: Calculate the similarity of visual features between candidate video clips and adjacent selected clips; Calculate the similarity of candidate video clips with adjacent selected clips in terms of semantic features; A coherence score is obtained based on the similarity in visual features and the similarity in semantic features.

6. The method for deduplicating video materials according to claim 1, characterized in that, The weighted result also incorporates a precise matching bonus. The selection of one or more target video clips from the plurality of candidate video clips, based on the weighted result of the tag matching degree and deduplication correlation score, includes: When a candidate video clip and a script clip perfectly match in a pre-defined multi-level semantic tagging system, the candidate video clip is given a bonus for accurate matching.

7. The method for deduplicating video materials according to claim 1, characterized in that, Also includes: After obtaining one or more target video clips corresponding to each script segment, multiple target video clips with the highest weighted results are retained as candidate video clips for each script segment. Based on the candidate video clips for each script segment, generate multiple video clip sequences that conform to the semantic order of the script; Calculate the overall repetition index for each combination of video clip sequences; Based on the overall repetition index, the final video material sequence is determined as the output from multiple video sequence combinations.

8. The method for deduplicating video materials according to claim 1, characterized in that, Also includes: After generating a short video based on one or more target video clips corresponding to each script segment, the similarity between the generated short video and the historically generated short videos is calculated. When the similarity between the generated short video and the historically generated short video is greater than or equal to the preset similarity threshold, the weight allocation of each scoring dimension is adjusted and the obtained script fragment and multiple candidate video material fragments that match its semantics are returned.

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