Video Segmentation via Subtitle-Mediated Scene Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional video segmentation methods are inaccurate, often segmenting videos in the middle of dialogues and failing to accurately represent scenes due to reliance on visual features alone, leading to incomplete and mismatched search results.
Innovation Solution
The method involves merging video segments based on common subtitle segments, performing scene analysis on frames and subtitles, and generating scene labels using machine learning algorithms to improve segmentation accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional scene recognition algorithms are used to segment video based on visual features, then video segmentation can be performed, but the segmentation accuracy deteriorates due to false scene changes from shooting styles or video effects
Solution Approach 1:
The patent introduces subtitle segments as an intermediary element to mediate between visual features and scene change detection. By using subtitle segments (which represent actual dialogue boundaries) as a reference, the system can identify and correct false scene changes detected by conventional visual feature analysis, thereby improving segmentation accuracy without losing productivity
Solution Approach 2:
The system implements feedback by comparing conventional scene change detections with subtitle segment boundaries. When discrepancies are found (false positives from shooting styles or effects), the system uses subtitle information to correct the segmentation, creating a feedback loop that continuously improves segmentation accuracy
2Productivity
If conventional scene recognition algorithms segment video based on visual features, then video can be divided into segments, but the completeness of video segments deteriorates when segments start or end in the middle of dialog
Solution Approach 1:
Subtitle segments serve as an intermediary that bridges visual segmentation and dialogue structure. By aligning video segments with subtitle segment boundaries, the system ensures that each video segment contains complete dialogue units, improving reliability while maintaining segmentation productivity
Solution Approach 2:
The patent merges two segmentation approaches: conventional visual feature-based segmentation and subtitle-based segmentation. By combining these methods and using subtitle boundaries to adjust visual segments, the system achieves both productivity (from visual analysis) and reliability (from dialogue completeness)
3Productivity
If scene labels are generated based only on visual features of video segments, then scene recognition can be performed, but the accuracy of scene labels deteriorates due to mismatched search results
Solution Approach 1:
The patent creates composite scene labels by combining visual feature analysis with subtitle content analysis. Instead of relying solely on visual features, the system integrates information from both modalities, creating a more accurate and comprehensive scene label that improves search accuracy while maintaining recognition productivity
Data Source
AI summary
Methods and systems for video segmentation and scene recognition are described. A video having a plurality of frames and a subtitle file associated with the video are received. Segmentation is performed on the video to generate a first set video frames comprising one or more video frames based on a frame-by-frame comparison of features in the frames of the video. Each video frame in the first includes a frame indicator which indicates at least a first start frame of the video frame. The subtitle file associated with the video is parsed to generate one or more subtitle segments based on a start and an end time of each dialogue in the subtitle file. A second set of video frames comprising one or more second video frames are generated based on the video frames of the first set of video frames and the e or more subtitle segments.