Media Content Classification Using Temporal Video Frame Analysis
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Solution Overview
Problem
Existing digital platforms face challenges in accurately classifying and detecting offensive content, such as video and audio, which can lead to the dissemination of harmful material, and often rely on incomplete metadata for content recommendations and search results.
Innovation Solution
Implementing machine-learning algorithms that analyze video and audio content by incorporating temporal dimensions and semantic representations to classify objects and events, thereby identifying potentially offensive content and controlling its sharing or recommending similar content based on classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning algorithms analyze all video and audio content to improve classification accuracy, then content classification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent segments video content into individual frames and audio content into discrete segments for analysis. The machine-learning model processes these segmented units independently to identify objects, events, and characteristics, then aggregates the results to form overall content classifications. This segmentation enables parallel processing and reduces the computational burden of analyzing complete video or audio files as single units.
Solution Approach 2:
The patent applies partial action by selectively analyzing only certain portions of content based on detected characteristics. When potentially offensive content is identified in a segment, the system focuses detailed analysis on that specific segment rather than uniformly processing the entire content file. This approach maintains high classification accuracy for problematic content while reducing overall processing time for benign content.
2Reliability
If comprehensive content analysis is performed to identify all offensive material, then content safety improves, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by performing initial analysis of content using machine-learning models to identify potentially offensive segments before applying more complex analysis rules. The system pre-processes video frames and audio segments to detect objects, events, and characteristics that may indicate offensive content, then uses these preliminary results to guide subsequent classification and moderation decisions.
Solution Approach 2:
The patent introduces an intermediary classification layer between raw content and final moderation decisions. The machine-learning model acts as an intermediary that transforms complex video and audio data into structured classifications of objects, events, and characteristics. This intermediary representation simplifies the subsequent analysis by providing standardized, interpretable outputs that are easier to evaluate against safety criteria.
3Measurement precision
If detailed metadata is collected for content recommendations, then recommendation quality improves, but data transmission and storage requirements increase
Solution Approach 1:
The patent extracts only the essential classification characteristics from analyzed content for use in recommendations and search results. Rather than transmitting or storing complete video and audio files along with all analysis metadata, the system extracts key classification data such as identified objects, events, and thematic characteristics. This extracted metadata is sufficient for generating accurate recommendations while significantly reducing data transmission and storage requirements.
Data Source
AI summary
Techniques are described that classify content, and control whether and how the content is shared based on the classification(s). In some examples, video content may be classified based on sequential image frames of the video, and time between the sequential image frames. Audio content may be classified based on combining classifications of multiple sound events in the audio signal. The classifications may be used to control how the content is shared, such as by preventing offensive content from being shared and/or outputting recommendations or search results based on the classifications.