Automated Video Highlight Generation via Frame Clustering
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Solution Overview
Problem
Existing methods for generating short preview videos from long main videos are inefficient, often requiring manual effort, relying on irrelevant initial frames, or randomly selecting scenes, which can misrepresent the content and fail to summarize the video effectively.
Innovation Solution
An automated method that identifies significant frames, computes video-level features, clusters frames based on similarity scores, and creates highlight videos that summarize the main video's most visually significant sequences, allowing for multiple highlight videos to be generated and presented without audio, with options for machine learning model training and A/B testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to select frames for preview videos, then the selection can be customized, but the process requires significant human resources and time
Solution Approach 1:
The system performs automatic frame selection without human intervention by computing video-level features, identifying significant frames through similarity scoring, and generating highlight videos autonomously. This self-service approach eliminates manual frame selection while maintaining high accuracy through algorithmic analysis of visual features and temporal dynamics.
Solution Approach 2:
The patent replaces manual mechanical selection processes with automated computational systems. Video-level features are extracted and compared against frame-level features using machine learning models, substituting human judgment with algorithmic decision-making that processes visual data efficiently and consistently.
2Ease of manufacture
If initial frames or random scenes are selected for preview videos, then the process is simple, but the preview may misrepresent the actual video content
Solution Approach 1:
The system performs preliminary analysis by computing video-level features from the entire video before selecting individual frames. This preliminary action ensures that frame selection is based on comprehensive understanding of the video content, temporal structure, and visual significance, preventing misrepresentation while maintaining automated simplicity.
Solution Approach 2:
The patent transforms the selection criterion from arbitrary (initial or random frames) to significance-based (frames with highest similarity scores to video-level features). This parameter change in selection methodology ensures that preview frames accurately represent the main video content while the automated process remains computationally efficient.
3Productivity
If a single highlight video is generated, then the processing is faster, but multiple different user preferences cannot be accommodated
Solution Approach 1:
The system segments the video into multiple highlight videos by identifying different significant frame clusters and generating separate preview videos from each cluster. This segmentation allows simultaneous provision of multiple personalized preview options without substantially increasing processing time, as the analysis is performed once on the original video.
Solution Approach 2:
The patent creates a universal system that generates multiple highlight videos serving different user preferences from a single analysis process. The same video-level features and significance metrics are used to generate diverse preview options, making the system adaptable to various user needs while maintaining processing efficiency through shared computational foundation.
Data Source
AI summary
A computer implemented method of generating at least one highlight video from an input video, comprising, using at least one processor for: identifying a plurality of significant frames of the input video, computing video-level features of the input video, selecting a plurality of subsets of the plurality of significant frames, for each subset, computing a similarity score indicating similarity between visual features of the subset and video-level features of the input video, clustering the input video into a plurality of clusters of sequential frames according to sequential positions within the video based on the similarity scores correlated with the plurality of significant frames, and creating at least one highlight video by selecting a cluster of sequential frames of the input video.