Video Scene Segmentation Using Adjacent Shot Embeddings
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Solution Overview
Problem
Current scene segmentation in videos is predominantly manual and lacks sophisticated automated solutions, making it inefficient and labor-intensive, especially when dealing with unlabeled video content.
Innovation Solution
A method and system that utilize a central processing unit and memory to segment scenes by analyzing and comparing key frames and shot embeddings, incorporating intra and inter context through mechanisms like multi-head self-attention and Kuleshov attention to accurately determine scene boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual scene segmentation is performed, then scene boundaries can be accurately identified with human understanding, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system performs automatic scene segmentation by having the video content itself provide the necessary information through extracted features and embeddings. The algorithm independently identifies scene boundaries by analyzing shot embeddings and calculating probability values, without requiring manual human intervention, thus achieving both automation and accuracy
Solution Approach 2:
The manual mechanical process of human scene segmentation is replaced with an automated computational system that uses machine learning models, shot embedding generation, and probability calculation algorithms to automatically identify scene boundaries, significantly improving efficiency while maintaining accuracy
2Productivity
If automated scene segmentation is attempted without sophisticated methods, then productivity increases through automation, but measurement precision and reliability of scene boundary detection deteriorate
Solution Approach 1:
Before performing scene boundary detection, the system performs preliminary actions including extracting key frames, generating shot embeddings that capture semantic information, and preparing probability calculation frameworks. These preliminary steps ensure that when automated segmentation occurs, it achieves high precision through well-prepared feature representations
Solution Approach 2:
The video is segmented into discrete shots, and each shot is further segmented into key frames that are then converted into patch embeddings. This multi-level segmentation approach allows the system to process video content in manageable units, extracting meaningful features at each level to improve overall detection accuracy while maintaining automation
3Measurement precision
If context information from adjacent shots is incorporated, then scene boundary detection precision improves through better understanding of scene transitions, but device complexity and processing requirements increase
Solution Approach 1:
The system merges information from multiple sources including the current shot embedding, adjacent shot embeddings, and probability calculations into a unified scene boundary detection framework. By combining these elements, the system achieves higher precision in detecting scene transitions while managing complexity through integrated processing
Data Source
AI summary
The present invention relates to a method of segmenting scenes in a video and a system therefor. Specifically, the present invention relates to a method and system for determining whether there is a transition between scenes and segmenting each of the scenes by extracting semantic characteristics of shots constituting a scene, particularly a specific shot and shots adjacent thereto, and comparing these characteristics.