Video Summarization via Topic-Based Frame Grouping
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Solution Overview
Problem
Users face significant time consumption when searching for content of interest in large collections of videos, as existing methods lack efficient ways to navigate and summarize video content.
Innovation Solution
A method and system that assign frames of a video to groups based on topics, generate similitude measurements, rank frames within groups, and select most representative frames for summarization, allowing for efficient content navigation and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search through videos to find content of interest, then they can locate specific content, but the time consumption is very high
Solution Approach 1:
The system performs preliminary actions by automatically generating video summaries, extracting key frames, and creating topic-based groupings before the user needs to search. This pre-processing allows users to quickly navigate to content of interest without manual searching, directly reducing time consumption while maintaining ease of operation.
Solution Approach 2:
The patent introduces an intermediary layer (video summary with key frames and topic groupings) between the user and the full video content. This intermediary structure enables efficient navigation and content discovery, resolving the contradiction by providing quick access points without requiring users to manually search through entire videos.
2Productivity
If the video is summarized by selecting key frames, then the content navigation becomes efficient, but the complexity of the summarization system increases
Solution Approach 1:
The video summarization system segments the video into topic-based groups and identifies key frames within each group. This segmentation approach enables efficient content navigation by organizing vast video content into manageable, topic-specific segments, directly improving productivity while the modular nature of segmentation helps manage system complexity.
Solution Approach 2:
The system changes parameters by selecting specific frames based on topic relevance and visual similarity metrics. By transforming the selection criterion from arbitrary or uniform sampling to topic-based parameter selection, the system achieves efficient navigation. The use of automated parameter-based selection reduces manual intervention needs, helping to balance productivity gains with system complexity.
Data Source
AI summary
Systems and methods for summarizing a video assign frames in a video to at least one of two or more groups based on a topic, generate a respective first similitude measurement for the frames in a group relative to the other frames in the group based on a feature, rank the frames in a group relative to one or more other frames in the group based on the respective first similitude measurement of the respective frames, and select a frame from each group as a most-representative frame based on the respective rank of the frames in a group relative to the other frames in the group.