AI Scene Detection Engine for Media Content Analysis
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Solution Overview
Problem
Conventional video segmentation engines perform automated identification of logical scenes in media content with low precision, requiring numerous configuration parameters and manual intervention, leading to inconsistent and incomplete segmentation, and lack scalability across media assets.
Innovation Solution
A system and method using artificial intelligence (AI) with an affinity propagation clustering algorithm that automatically detects and marks logical scenes in media content by combining visual and audio features, dynamically selecting the number of scenes based on feature similarity, and performing noise reduction and post-processing to correct scene boundary errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video segmentation engines are used for automated scene detection, then productivity is improved through automation, but measurement precision deteriorates due to low precision in scene boundary detection
Solution Approach 1:
The patent segments the video analysis problem into multiple independent components: shot detection (identifying individual shots), scene detection (grouping shots into scenes), and boundary refinement (correcting scene boundaries). This segmentation allows each component to be optimized independently, improving overall precision while maintaining automation.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines scene boundaries based on detected shot transitions and visual/audio similarity metrics. The boundary refinement process uses feedback from initial detection to iteratively improve precision, resolving the contradiction between automated productivity and measurement precision.
2Measurement precision
If manual video segmentation is performed by trained human operators, then measurement precision is improved through expert judgment, but productivity deteriorates due to time-intensive and expensive processes
Solution Approach 1:
The patent enables the video segmentation system to perform self-correction and self-refinement of scene boundaries using automated algorithms. The system independently identifies and corrects boundary errors without requiring manual intervention, achieving both high precision and automated productivity through self-service capabilities.
Solution Approach 2:
The patent replaces the mechanical human operator system with an automated computational system that uses visual and audio feature analysis. This substitution eliminates the need for manual labor while maintaining or exceeding the precision that human experts could achieve, resolving the productivity-precision trade-off.
3Ease of operation
If conventional scene detection algorithms are used, then ease of operation is improved through simple implementation, but device complexity increases due to numerous configuration parameters
Solution Approach 1:
The patent extracts and removes the complex configuration parameters from the scene detection algorithm, replacing them with automated parameter selection based on the video content itself. This extraction simplifies the user interface and operation while maintaining detection accuracy, resolving the contradiction between ease of operation and device complexity.
Solution Approach 2:
The patent dynamically changes detection parameters based on the specific characteristics of each video asset rather than requiring fixed configuration. The system automatically adapts parameters such as similarity thresholds and boundary refinement criteria to match the content being analyzed, reducing the need for manual configuration while improving performance.
4Productivity
If conventional video segmentation engines are deployed across multiple media assets, then productivity is improved through scalability, but measurement precision deteriorates due to inconsistent performance across different asset types
Solution Approach 1:
The patent creates a universal scene detection system that can handle multiple types of media assets (videos, webinars, podcasts with video) with a single unified algorithm. The system automatically adapts to different asset types and durations, maintaining consistent precision across diverse content while enabling scalable deployment across the entire media library.
Data Source
AI summary
A system including a scene detection engine (SDE) and a method for automatically detecting and marking logical scenes in media content using artificial intelligence are provided. The SDE detects boundaries of shots in media content. The SDE generates an image similarity matrix by extracting color features from a middle frame of each shot. The SDE generates an audio similarity matrix by extracting audio features of each shot. The SDE generates a resultant similarity matrix from the image and audio similarity matrices. The SDE executes a clustering algorithm using the resultant similarity matrix with reduced or no noise to generate an ordered sequence of shots that define a boundary of each scene of the media content. The SDE executes a linear traversal algorithm on the shots that define the boundary of each scene for correcting errors in the defined boundary of each scene.