Machine-Learned Video Shot Matching for Automatic Trailer Detection
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Solution Overview
Problem
Existing systems for automatically generating movie trailers require significant human intervention and lack the ability to analyze full-length films, track storylines, distinguish environments, and select shots based on emotional value, leading to inefficiencies in the trailer creation process.
Innovation Solution
A machine learning model is trained to recognize key moments in films or television shows by matching video shots using factors like similar objects, motion, and backgrounds, generating similarity scores, and identifying desirable shots for trailers, hook clips, or artwork, without human supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automatic trailer generation systems are used, then some automation is achieved, but human supervision is still required and the systems lack the ability to analyze full-length films, track storylines, distinguish environments, or select shots based on emotional value
Solution Approach 1:
The system segments full-length films into discrete video shots and groups them into scenes based on visual similarity and temporal continuity. This segmentation enables automated analysis of storyline progression and emotional arcs while maintaining the ability to distinguish different environments and contexts within the film.
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a bridge between raw video content and trailer generation decisions. This model learns to identify key moments, emotions, and narrative structures by training on matched video shots from films and their corresponding trailers, enabling automated selection without human supervision.
2Reliability
If manual trailer production is used, then high quality and emotional value selection are achieved, but the process is lengthy and highly involved requiring many different people
Solution Approach 1:
The system implements self-service automation where the machine learning model independently analyzes films, identifies key moments, and generates trailers without human intervention. The model trains on matched video shots to learn emotional value and narrative structure, enabling it to autonomously produce high-quality trailers while dramatically increasing production speed.
Solution Approach 2:
The system changes the parameters of trailer generation by using learned embeddings and similarity scores to automatically evaluate video shots based on multiple factors including emotional content, narrative importance, and visual appeal. This transforms the manual artistic judgment process into an automated multi-parameter optimization problem.
3Extent of automation
If VHD process is used, then video clips are extracted automatically, but human supervision is highly required to identify and highlight moments as specific actions or events
Solution Approach 1:
The system extracts the essential features and patterns from matched video shots and trailers, removing the need for human annotation of specific actions and events. By learning from the extracted features including visual similarity, motion patterns, and contextual relationships, the system autonomously identifies key moments without requiring human supervision to categorize them.
4Measurement precision
If traditional systems are used to analyze full-length films, then some analysis is performed, but the systems lack the power and precision to track storyline, distinguish environments, or select shots based on emotional value
Solution Approach 1:
The system replaces traditional mechanical analysis methods with machine learning-based automated analysis. The model learns to track storyline progression, distinguish environments, and evaluate emotional value by analyzing patterns in video shots and their relationships, achieving high precision in film analysis through automated neural network processing rather than manual or rule-based systems.
Data Source
AI summary
The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.