Automated Video Segmentation Using Multi-Modal Feature Extraction
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Solution Overview
Problem
Conventional video segmentation methods are time-intensive, costly, and subjective, leading to inconsistent, inaccurate, and incomplete results due to manual indexing, especially when dealing with gradual transitions or soft cuts in digital videos.
Innovation Solution
Automated video segmentation by extracting visual, audio, and textual features from frames, using similarity metrics to detect abrupt and gradual transitions, and employing graph representation with a minimum cut algorithm to segment videos into scenes, along with additional metadata processing for annotation and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual video segmentation is used to generate an index or description of video content, then the video can be organized and managed, but the process becomes time-intensive and prohibitively costly
Solution Approach 1:
The patent replaces manual mechanical segmentation with automated computer-based analysis. The system extracts visual, audio, and textual features from video frames and uses algorithmic processing to detect scene transitions, eliminating the need for human operators to manually review and segment video content while maintaining accurate organization capabilities
Solution Approach 2:
The video segmentation system performs self-service by automatically analyzing its own video content without external human intervention. The automated extraction of features and detection of transitions enables the system to independently generate scene boundaries and organize video data, making the process both time-efficient and cost-effective
2Ease of manufacture
If manual video segmentation is used to index video content, then the video can be organized, but the segmentation becomes highly subjective and inconsistent
Solution Approach 1:
The patent replaces subjective human judgment with objective algorithmic analysis. The automated system consistently applies the same feature extraction and transition detection algorithms to all video content, eliminating variability introduced by different human operators and ensuring reliable, reproducible segmentation results across diverse video datasets
Solution Approach 2:
The system transforms subjective segmentation into objective parameter-based analysis by quantifying visual, audio, and textual features. By measuring specific parameters such as color histograms, audio energy, and text frequency, the system establishes consistent, measurable criteria for scene transitions that eliminate subjectivity and improve reliability
3Loss of information
If manual video segmentation is used to create video indexes, then video content can be described, but the results become inaccurate and incomplete
Solution Approach 1:
The patent applies multi-modal segmentation by dividing video analysis into distinct feature domains: visual features from video frames, audio features from soundtracks, and textual features from captions or transcripts. This comprehensive segmentation approach ensures that no important information is missed and provides accurate, complete video descriptions through integrated analysis of all modalities
Solution Approach 2:
The system achieves universal accuracy by implementing a multi-functional analysis framework that simultaneously processes visual, audio, and textual information. This multi-functional approach ensures that scene transitions detected in any modality contribute to the overall segmentation accuracy, making the system robust and complete across different types of video content
Data Source
AI summary
A video segmentation system can be utilized to automate segmentation of digital video content. Features corresponding to visual, audio, and/or textual content of the video can be extracted from frames of the video. The extracted features of adjacent frames are compared according to a similarity measure to determine boundaries of a first set of shots or video segments distinguished by abrupt transitions. The first set of shots is analyzed according to certain heuristics to recognize a second set of shots distinguished by gradual transitions. Key frames can be extracted from the first and second set of shots, and the key frames can be used by the video segmentation system to group the first and second set of shots by scene. Additional processing can be performed to associate metadata, such as names of actors or titles of songs, with the detected scenes.