Automated Video Frame Selection via Face Embedding Clustering
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Solution Overview
Problem
The manual process of selecting representative video frames for artwork in video streaming services is time-consuming and inefficient, unable to scale with the rapid growth of media titles, requiring significant resource overhead and human intervention.
Innovation Solution
An automated technique using embedding models to generate face embeddings, cluster characters, compute prominence and interaction scores, and select frames based on these scores to identify representative frames for artwork, leveraging machine learning models for efficiency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual frame selection is used, then quality of artwork representation is maintained, but time consumption and resource overhead increase significantly
Solution Approach 1:
The patent replaces the manual mechanical selection process with an automated machine learning system that uses embedding models to generate face embeddings, cluster characters, compute prominence scores, and automatically select representative frames for artwork, eliminating the need for human media specialists to manually review and select frames
Solution Approach 2:
The system enables self-service by automatically performing the frame selection task without human intervention. The machine learning model independently analyzes video content, identifies important characters and frames, and generates artwork candidates, allowing the system to serve itself rather than requiring human expertise for each selection
2Measurement precision
If manual frame selection process is used, then selection accuracy is maintained, but scalability with increasing number of media titles is limited
Solution Approach 1:
The patent replaces the manual selection process with an automated machine learning system that scales efficiently. The embedding model and clustering algorithm can process large numbers of media titles automatically, enabling the system to scale with growing content libraries without requiring proportional increases in human resources
Solution Approach 2:
The system changes the approach from human-based selection to algorithm-based selection, where parameters such as embedding dimensions, cluster thresholds, and prominence score cutoffs can be adjusted to handle varying scales of media titles. This allows the same system to efficiently process from a few to millions of titles
3Reliability
If manual frame selection is used, then quality control is maintained, but productivity decreases due to time-consuming processes
Solution Approach 1:
The patent replaces manual quality control with automated machine learning-based quality assessment. The embedding model and clustering algorithm objectively evaluate frame quality and character importance, providing consistent and reliable quality control that is not subject to human fatigue or variability, while dramatically increasing productivity
Solution Approach 2:
The system performs self-service quality control by automatically evaluating and selecting frames based on learned patterns and prominence metrics, eliminating the need for human quality assurance and enabling rapid artwork production at scale
Data Source
AI summary
One embodiment of the present invention sets forth a technique for selecting a frame of video content that is representative of a media title. The technique includes applying an embedding model to a plurality of faces included in a set of frames of the video content to generate a plurality of face embeddings. The technique also includes aggregating the plurality of face embeddings into a plurality of clusters representing a plurality of characters included in the media title. The technique further includes computing a plurality of prominence scores for the plurality of characters based on one or more attributes of the plurality of clusters, and selecting, from the set of frames, a frame of video content as representative of the media title based on one or more prominence scores for one or more characters included in the frame.