Video Clip Selection Using Attribute Distributions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating short videos from long ones, such as video summarization and AI-based techniques, face challenges in accurately matching video content and plot, leading to low matching accuracy that does not meet commercial standards, particularly in converting long videos into high-quality short videos like movie trailers.
Innovation Solution
A method involving segmentation of long videos into short clips based on content changes, attribute analysis using deep neural networks, and selection and combination of clips using attribute distributions to generate high-quality short videos that meet specific genre requirements, employing direct and weighted sampling methods to optimize the final video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI-based video summarization methods are used to convert long videos into short videos, then the production efficiency is improved, but the matching accuracy of video content and plot deteriorates
Solution Approach 1:
The long video is segmented into multiple short clips based on content changes and scene transitions. Each clip is independently analyzed and selected based on attribute distributions that match the target genre requirements, enabling both efficient processing and accurate content matching
Solution Approach 2:
The method changes the selection parameters from simple temporal sampling to attribute-based weighted sampling. By analyzing attributes such as scene type, character actions, and plot importance, the system selects clips that accurately represent the source video's content and plot, significantly improving matching accuracy while maintaining productivity
2Device complexity
If random sampling methods are used to select video clips, then the selection process is simplified, but the quality of generated short videos deteriorates
Solution Approach 1:
The system incorporates feedback from attribute analysis of both source and target videos. By continuously comparing the attributes of selected clips against the target genre requirements and adjusting the selection weights accordingly, the method ensures high video quality while keeping the process manageable through automated feedback loops
Solution Approach 2:
The selection process transitions from uniform random sampling to attribute-weighted sampling. Clips are assigned weights based on their attribute matching scores, ensuring that high-quality clips representing the source video's essence are prioritized, thereby improving video quality without excessive complexity
3Measurement precision
If traditional video editing methods are used, then the control over video content is improved, but the time consumption increases
Solution Approach 1:
The system performs preliminary analysis of the long video to identify and segment relevant clips based on content changes and plot importance. By pre-processing and pre-selecting candidate clips according to attribute distributions before final assembly, the method reduces time consumption while maintaining precise content control
Solution Approach 2:
The automated system performs self-service by independently analyzing video attributes, selecting appropriate clips, and assembling the final short video. This self-service approach eliminates the need for manual frame-by-frame review and selection, significantly reducing time consumption while maintaining high content control accuracy through automated attribute-based decision making
Data Source
AI summary
Methods and apparatuses are provided for editing and generating a short video based upon a long video. The method includes: obtaining a plurality of short source video clips as candidate video clips; obtaining attributes of each short source video clip; obtaining a plurality of target base videos according to a target genre, processing the plurality of target base videos by splitting each target base video into a plurality of short target base video clips, and obtaining attributes of each short target base video clip; selecting short target video clips from the plurality of short source video clips, based on distribution of the attributes obtained for the plurality of the short source video clips and the plurality of short target base video clips; and editing and combining the short target video clips selected from the plurality of short source video clips, to obtain a target video.