Scene Break Detection via Multimedia Embedding
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Solution Overview
Problem
Existing methods for detecting scene breaks in media content are inefficient and prone to variability, as they rely heavily on human intervention, which can lead to inconsistent placement of cue points for targeted media content.
Innovation Solution
The system segments media content into units by detecting unit boundaries and encodes these units into a multimedia representation in an embedding space across different media modalities. A sequence classifier then identifies scene boundaries based on these representations, improving accuracy and reducing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If human intervention is used to detect scene breaks, then the process is simple to implement, but the accuracy and consistency of scene boundary identification deteriorates due to variability and human error
Solution Approach 1:
The patent replaces the mechanical human decision-making process with an automated machine learning system. The system uses feature encoders to extract visual, audio, and text features, processes them through a sequence classifier trained to identify scene boundaries, and outputs automated scene break detections. This substitution eliminates human variability while maintaining implementation feasibility through standardized computational processes.
Solution Approach 2:
The system enables self-service by allowing the media content to be automatically analyzed without human intervention. The sequence classifier independently processes the encoded features and identifies scene boundaries based on learned patterns from training data, making the system autonomous and eliminating the need for manual scene break detection.
2Measurement precision
If automated feature encoding and sequence classification are applied, then the accuracy of scene boundary identification improves, but the device complexity and computational resources required increase
Solution Approach 1:
The patent segments the complex scene break detection task into distinct modular components: feature encoding (extracting visual, audio, and text features), sequence classification (identifying scene boundaries), and post-processing (refining detections). Each module can be independently developed, trained, and optimized, reducing overall system complexity while maintaining high accuracy through specialized processing at each stage.
3Reliability
If multiple media modalities are encoded into embedding space, then the relevance of targeted media content insertion improves, but the computational energy and processing time required increase
Solution Approach 1:
The patent performs preliminary encoding of media content into embedding space during an offline preprocessing stage. Feature encoders extract and encode visual, audio, and text features from the entire media content before insertion points are needed. This preliminary action stores the encoded representations for rapid retrieval and comparison during targeted content insertion, reducing real-time computational energy requirements while maintaining high relevance through multi-modal feature integration.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for identifying scene breaks in media content. An example method comprises segmenting media content into a sequence of units by detecting unit boundaries. One or more feature encoders are applied to generate in an embedding space a multimedia representation of features of each unit in the sequence across different media modalities. A sequence classifier is applied to identify whether a unit boundary is a scene boundary based on the multimedia representation of units in the embedding space in at least a subset of the sequence of units.