Video Classifier System for Content Source Identification
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Solution Overview
Problem
Video hosting services face difficulties in identifying and managing content sources for participant-observer videos, which lack a reference version, making it challenging to determine the source of the content and enforce usage policies.
Innovation Solution
A video classifier system is implemented, using instance classifiers trained on specific content sources to identify the source of participant-observer videos through feature extraction and classification, along with genre classifiers to categorize videos, allowing for accurate content source identification and policy enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fingerprinting methods are used to match videos, then broadcast-type content can be identified accurately, but participant-observer videos cannot be matched because their content varies significantly between different copies
Solution Approach 1:
The patent segments the video content into multiple features including visual features (color histograms, motion detection, edge detection), audio features (spectral analysis, zero-crossing rate), and metadata features. By dividing the content into these separable components, the system can identify and compare key elements across different P/O videos while tolerating variations in other aspects, thereby resolving the contradiction between matching accuracy and adaptability to varied video types
Solution Approach 2:
The patent transforms the video content from raw pixel data into multiple parameter representations including color space conversions (RGB to HSV/YCbCr), frequency domain transformations (FFT for audio), and spatial feature extractions. These parameter changes enable the system to capture essential content characteristics while being invariant to the variations inherent in P/O videos, allowing accurate matching across different recording conditions
2Measurement precision
If multiple features are extracted and analyzed to identify content sources, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the classification task into multiple independent instance classifiers, each trained to recognize a specific content source. Each classifier processes the same set of features but focuses on identifying one particular source. This segmentation of the classification function reduces the complexity of individual classifiers while maintaining high overall identification accuracy through the collective output of multiple specialized classifiers
Solution Approach 2:
The patent introduces an intermediary scoring mechanism that aggregates results from multiple instance classifiers. Each classifier produces a score indicating the likelihood of its associated content source, and these scores are combined to determine the final identification. This intermediary scoring layer simplifies the decision-making process by transforming complex multi-classifier outputs into a manageable ranking system
3Reliability
If feature extraction is performed on all videos to enable classification, then content identification capability is enhanced, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most discriminative features from video content rather than processing all possible characteristics. The feature extraction focuses on key elements such as dominant colors, motion patterns, and audio signatures that are most likely to identify content sources. This partial action approach maintains reliable identification capability while significantly reducing processing time compared to comprehensive feature analysis
Solution Approach 2:
The patent performs feature extraction and classifier training in advance during an offline phase. Once the instance classifiers are trained on labeled content, they can rapidly process new videos by comparing extracted features against the pre-learned models. This preliminary action separates the computationally intensive training process from the real-time classification process, enabling fast content identification when videos are uploaded or analyzed
Data Source
AI summary
A video hosting service comprising video classifiers that identify content sources of content included in videos uploaded to the video hosting service. Identifying the content source allows a content owner of the content source to claim ownership of videos that include content based on the content source. Usage policies associated with the content owners are applied to the uploaded videos that describe how the video hosting service is to treat the videos.