Automated Video Classification via Frame Matching
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Solution Overview
Problem
Current methods for tracking the spread of videos on the internet are manual, time-consuming, and prone to errors, lacking automation to determine whether a video copy represents the source video.
Innovation Solution
An automated method using feature transforms like ORB to identify matching static image frames and compare metadata for temporal order, classifying candidate videos as instances or variants of source videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review is used to track video spread, then accuracy can be maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical review with automated computer-based video classification using feature extraction and matching algorithms. The system automatically compares candidate videos against source videos by extracting visual features and computing similarity metrics, eliminating the need for human reviewers while maintaining classification accuracy.
Solution Approach 2:
The system enables self-service automated classification where the video analysis system performs its own evaluation without human intervention. The automated pipeline extracts features, matches frames, computes temporal order similarity, and classifies videos independently, allowing the system to serve itself rather than requiring manual processing.
2Reliability
If manual review is used to determine video instances, then measurement can be performed, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The patent substitutes manual mechanical evaluation with automated computational algorithms that systematically compare video frames using feature extraction techniques. This replacement reduces human error and increases reliability while managing complexity through standardized automated procedures rather than manual processes.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results are validated and refined through iterative processing. The automated system compares candidate videos against established source videos, adjusts classification decisions based on similarity metrics, and maintains reliable tracking through continuous validation loops.
3Productivity
If automated classification is implemented, then time efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the video classification task into distinct manageable stages: frame sampling, feature extraction, frame matching, temporal order comparison, and classification decision. This segmentation reduces overall system complexity by breaking down the complex automated process into modular components that can be implemented and maintained more easily.
Solution Approach 2:
The system uses copying of visual features and frame data to enable automated comparison. By extracting and copying characteristic features from source videos and comparing them against candidate videos, the system achieves efficient automated classification without requiring complex real-time processing, thus improving productivity while managing complexity.
Data Source
AI summary
A method to automatically classify video content retrieved over the Internet and, in particular, to determine whether a candidate video is an instance of a source video. The method begins by sampling static image frames from each of the candidate video and the source video. Based on the sampled static image frames, a set of static image frames in the candidate video that match a corresponding set of static image frames in the source video are then identified. Metadata associated with the sets of matching static image frames are then compared to determine how closely a temporal order of the set of static image frames in the candidate video matches the temporal order of the set of static image frames in the source video. Based on a measure of temporal order match, a classification is then applied to the candidate video to complete the method.