Video Content Segmentation Using Graph-Based Partitioning
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Solution Overview
Problem
Current video segmentation and content identification technologies are limited in scalability and efficiency, particularly for large-scale deployments and live broadcasts, as they rely on logo detection and black frames, which are infrequent or absent in many channels, and are not designed for real-time monitoring during live broadcasts.
Innovation Solution
The method involves generating fingerprints of incoming video content, tracking time-aligned changes, and using graph-based partitioning to classify content segments, allowing for accurate segmentation and replacement of advertisements in real-time, even in the absence of logo detection, by associating nodes with similarity scores and edge weights in a graph structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If logo detection and black frame methods are used for video segmentation, then content identification can be performed, but the method is not suitable for large-scale deployments and live broadcasts due to infrequent or absent logos and black frames
Solution Approach 1:
The video content is divided into segments using graph-based partitioning where nodes represent content segments and edges represent transitions. This segmentation approach works reliably for both recorded and live content without depending on logos or black frames, enabling universal applicability across different broadcast types.
Solution Approach 2:
A graph structure is introduced as an intermediary representation between raw video frames and content identification. The graph nodes and edges capture content transitions and similarities, serving as a mediator that enables reliable segmentation and identification regardless of whether logos or black frames are present in the source material.
2Loss of time
If traditional content identification methods are used, then content can be identified after recording, but real-time monitoring and segmentation during live broadcasts cannot be achieved
Solution Approach 1:
The system performs preliminary fingerprinting and graph construction on incoming video streams in real-time. By continuously building the content graph as video arrives rather than processing after recording, the system enables both low-latency identification and efficient real-time monitoring simultaneously.
Solution Approach 2:
The graph-based segmentation operates continuously on live video streams without interruption or batch processing. This continuous operation maintains real-time monitoring efficiency while minimizing processing delay, as the system constantly updates content segments and transitions as new video data arrives.
3Measurement precision
If graph-based partitioning with similarity scores is used, then segmentation accuracy is improved, but computational effort increases
Solution Approach 1:
The system computes similarity scores selectively for graph edge construction rather than comparing all possible node pairs. By calculating similarities only for adjacent or potentially related segments based on content features, the system achieves high segmentation accuracy while reducing computational effort through targeted rather than exhaustive comparison.
Data Source
AI summary
Content segmentation, categorization and identification methods are described. Content tracking approaches are illustrated that are suitable for large scale deployment. Time-aligned applications such as multi-language selection, customized advertisements, second screen services and content monitoring applications can be economically deployed at large scales. A client performs fingerprinting, scene change detection, audio turn detection, and logo detection on incoming video and gathers database search results, logos and text to identify and segment video streams into content, promos, and commercials. A learning engine is configured to learn rules for optimal identification and segmentation at each client for each channel and program. Content sensed at the client site is tracked with reduced computation and applications are executed with timing precision. A user interface for time-aligned publishing of content and subsequent usage and interaction on one or more displays is also described.