Graph-Based Video Segmentation Using Artificial Neural Networks
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Solution Overview
Problem
Conventional approaches for video segmentation are tedious, inefficient, and unscalable, often requiring human curation and evaluation to identify and distinguish between segments of video programs.
Innovation Solution
An automated video segmentation system using machine learning techniques, specifically employing artificial neural networks (ANNs) to analyze video graphs generated from frame sequences, predicts clustering of frames according to video segments and segment classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional approaches for video segmentation are used, then human curation and evaluation can identify and distinguish between segments, but the process is tedious, inefficient, and unscalable
Solution Approach 1:
The patent replaces manual human curation and evaluation with an automated machine learning system that uses artificial neural networks to perform video segmentation. The system processes video frames, constructs video graphs, and automatically identifies segments without human intervention, thereby eliminating the tedious and inefficient manual process while significantly improving productivity and scalability
Solution Approach 2:
The video segmentation system performs self-service by automatically analyzing video content, constructing graphs from video frames, training neural networks on the data, and generating segmentation results without requiring human operators. The system serves itself through automated data processing, model training, and segment identification, making the process scalable and efficient
2Productivity
If automated video segmentation using machine learning is implemented, then efficiency and scalability improve, but system complexity increases
Solution Approach 1:
The patent divides the complex video segmentation task into distinct modular components: video frame processing, graph construction from frames, neural network training, and segment identification. Each component handles a specific aspect of the analysis, making the overall system more manageable and maintainable despite the increased automation capability
Solution Approach 2:
The artificial neural network serves multiple functions within the system: it processes video graph data, identifies video segments, and can be trained on diverse video content. This multi-functionality reduces the need for separate specialized systems for different segmentation tasks, managing complexity while maintaining versatility
Data Source
AI summary
Methods and systems for automated video segmentation are disclosed. A sequence of video frames having video segments of contextually-related sub-sequences may be received. Each frame may be labeled according to segment and segment class. A video graph may be constructed in which each node corresponds to a different frame, and each edge connects a different pair of nodes, and is associated with a time between video frames and a similarity metric of the connected frames. An artificial neural network (ANN) may be trained to predict both labels for the nodes and clusters of the nodes corresponding to predicted membership among the segments, using the video graph as input to the ANN, and ground-truth clusters of ground-truth labeled nodes. The ANN may be further trained to predict segment classes of the predicted clusters, using the segment classes as ground truths. The trained ANN may be configured for application runtime video sequences.