Video Editing Apparatus for Personalized Summary Generation
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Solution Overview
Problem
Current video editing technologies lack the ability to effectively generate summary information from video data based on user preferences, specifically selecting and combining video segments that match desired concepts and superordinate concepts with high confidence scores.
Innovation Solution
A video editing apparatus that includes a storing unit for video data with confidence scores, an input unit for user preference coefficients, a segment selection unit that chooses segments based on these preferences, and a generation unit that creates summary information by calculating video scores for subordinate and superordinate concepts, allowing for the generation of digest videos or images that align with user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video data is divided into multiple segments and analyzed for concept inclusion, then the precision of matching user preferences is improved, but the complexity of the editing apparatus increases
Solution Approach 1:
The video data is divided into multiple segments, and each segment is analyzed independently for concept inclusion with confidence scores. This segmentation allows precise matching of user preferences by evaluating each segment separately while maintaining manageable processing complexity through modular analysis.
2Measurement precision
If multiple concepts and superordinate concepts are evaluated for each segment, then the accuracy of summary information generation is improved, but the processing time increases
Solution Approach 1:
Concept extraction and confidence score calculation are performed in advance for all video segments before final summary generation. This preliminary analysis stores concept inclusion probabilities, enabling rapid matching against user preferences during summary creation without re-processing video data.
Solution Approach 2:
Only the essential concept inclusion probabilities and confidence scores are extracted and stored for each segment, separating this pre-computed information from the full video data. This extraction allows efficient querying and matching during summary generation without handling the complete video content again.
3Adaptability or versatility
If the system selects segments based on multiple preference coefficients, then the personalization of summary information is improved, but the difficulty of detecting and measuring preferences increases
Solution Approach 1:
User preferences are represented as adjustable coefficient parameters for different concepts and superordinate concepts. By transforming qualitative preferences into quantitative coefficients, the system enables flexible personalization through parameter adjustment while simplifying the measurement and comparison of preference strength across multiple dimensions.
Data Source
AI summary
A video editing apparatus includes a storing unit, an input unit, a segment selection unit, and a generation unit. The storing unit stores video data along with video attribute information indicating, for each concept, a confidence score that the concept is included in each of segments into which the video data has been divided. The input unit inputs, as preference information, a coefficient of each concept desired to be included in summary information and a coefficient of a superordinate concept of the concept desired to be included in the summary information. The segment selection unit selects, based on the input preference information, at least one segment that matches the preference information, from among plural segments of the stored video data. The generation unit generates, based on video of the at least one selected segment, summary information representing contents of the video.