Video Segment Theme Matching for Faster Content Retrieval
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Solution Overview
Problem
The increasing volume of video data poses challenges in efficiently determining relevant segments and themes within videos, leading to information overload and difficulty in quickly identifying desired content.
Innovation Solution
A method involving segment acquisition, theme information extraction, and determining an association degree between video segments and theme information using a neural network model, including BERT, LSTM, and CRF, to identify and generate video abstracts based on theme consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video data is segmented and analyzed to identify relevant segments, then content retrieval accuracy is improved, but processing time increases
Solution Approach 1:
The video data is divided into multiple segments, and the system selectively processes only those segments that are relevant to the given theme information. This segmentation approach allows the system to maintain high retrieval accuracy by thoroughly analyzing relevant portions while avoiding unnecessary processing of irrelevant content, thus resolving the contradiction between accuracy and processing time.
Solution Approach 2:
The system applies partial action by determining association degrees for only the necessary video segments rather than processing the entire video data uniformly. By identifying and processing only the portions of video data that have meaningful association with the theme information, the system achieves accurate content retrieval without the time cost of complete data processing.
2Measurement precision
If comprehensive video analysis is performed to determine theme association, then retrieval accuracy is improved, but computational complexity increases
Solution Approach 1:
The computational task is segmented into identifying video segments with meaningful associations to the theme information. Rather than performing comprehensive analysis on all video data, the system divides the problem into manageable segments and focuses computational resources on those segments that contribute to accurate theme association determination.
Solution Approach 2:
The system extracts and processes only the essential features and segments of video data that are relevant to theme association. By taking out and analyzing only the necessary components rather than processing the entire video dataset comprehensively, the system achieves accurate theme association determination while reducing computational complexity.
3Loss of information
If all video data is processed to ensure complete information extraction, then information completeness is improved, but processing efficiency decreases
Solution Approach 1:
The system performs partial processing on video data by identifying and analyzing only those segments that contain information relevant to the theme. This approach ensures that all necessary information for accurate theme association is extracted while avoiding the inefficiency of processing the entire video dataset, thus maintaining information completeness without sacrificing processing efficiency.
Solution Approach 2:
The system autonomously identifies and processes only the video segments that contain meaningful information related to the theme. By self-selecting the relevant portions of video data for processing, the system ensures that no critical information is missed while maintaining high processing efficiency through intelligent selection rather than exhaustive processing.
Data Source
AI summary
A method for processing video data includes: acquiring a target segment of video data to be extracted; acquiring theme information to be extracted; and determining an association degree between the target segment and the theme information based on segment information of the video data and a relationship between the target segment and the video data.