Automated Video Scene Classification via Audio Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Viewers face frustration in finding specific scenes in video content, such as movies or TV programs, due to the lack of efficient methods for segmenting and classifying scenes, especially when they cannot recall the timing or visual appearance of the scene, leading to labor-intensive and error-prone manual classification processes.
Innovation Solution
The implementation of an automated system that segments video content using audio analysis, combined with visual understanding and machine learning models, to classify scenes into categories like comedic, inspirational, or musical, allowing for the generation of playlists of specific segments, enabling faster-than-real-time processing and accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of video scenes is performed, then classification accuracy can be maintained, but processing time and labor intensity increase significantly
Solution Approach 1:
The patent segments video content into atomic audio segments and processes them in parallel using multiple machine learning models. This segmentation allows the system to handle large video files efficiently while maintaining classification accuracy through specialized models for different scene types.
Solution Approach 2:
The patent replaces manual mechanical classification with automated machine learning models that analyze audio and visual features. This substitution eliminates human labor while maintaining or improving classification accuracy through consistent, algorithmic processing of video content.
2Productivity
If automated processing is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The system segments video processing into distinct analytical components handled by specialized machine learning models. This modular segmentation enables parallel processing that increases speed while keeping each individual model relatively simple and manageable.
Solution Approach 2:
The patent implements a universal processing framework that handles multiple scene types and classifications through a single integrated system. This multi-functional approach increases processing speed by avoiding multiple separate systems while the modularity keeps complexity manageable.
3Measurement precision
If comprehensive scene analysis is performed, then classification accuracy improves, but computational resources required increase
Solution Approach 1:
The patent segments video content into atomic audio segments and processes them in parallel using multiple specialized machine learning models. This segmentation enables comprehensive analysis to be distributed across multiple smaller, more efficient processing units, reducing overall computational resource requirements while maintaining accuracy.
Solution Approach 2:
The system applies different levels of analysis to different segments based on their characteristics. By selectively applying comprehensive analysis only where needed and using simpler classification for other segments, the system maintains high overall accuracy while reducing total computational resource consumption.
Data Source
AI summary
Disclosed are various embodiments for segmenting and classifying video content using conversation. In one embodiment, a plurality of segments of a video content item are generated by analyzing audio accompanying the video content item. A subset of the plurality of segments that correspond to conversation segments are selected. Individual segments of the subset of the plurality of segments are processed to determine whether a classification applies to the individual segments. A list of segments of the video content item to which the classification applies is generated.