Video Segment Labeling Using Fingerprint Repetition Patterns
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Solution Overview
Problem
Existing systems struggle to accurately identify and label program segments versus advertisement segments within video content, particularly in scenarios where advertisements are inserted between program segments, which complicates fingerprinting and content management.
Innovation Solution
A computing system uses fingerprint repetition data and closed captioning data to classify video content segments as program or advertisement segments based on unique reference identifiers and closed captioning patterns, and stores these segments accordingly in data files, utilizing an electronic program guide for timestamp verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprint repetition data and closed captioning data are used to classify video segments, then segment identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments video content into distinct program segments and advertisement segments by analyzing fingerprint repetition data and closed captioning patterns. Each segment type is identified through specific classification rules applied to the segmented data, enabling precise identification without requiring complex holistic analysis of the entire video stream.
Solution Approach 2:
Fingerprint repetition data and closed captioning data serve as intermediary elements that bridge the gap between raw video content and segment classification. These intermediaries provide structured information that simplifies the classification process, allowing the system to accurately distinguish program segments from advertisement segments through pattern recognition rather than complex direct analysis.
2Measurement precision
If multiple data types are analyzed for segment classification, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis by extracting fingerprint repetition data and closed captioning data before conducting the actual segment classification. This preliminary action prepares the data in advance, organizing it into structured formats that can be quickly processed during classification, thereby reducing the time required for the main classification operation while maintaining high accuracy.
Solution Approach 2:
The system transforms raw video data into different parameter representations, specifically fingerprint repetition patterns and closed captioning text features. By changing the parameters from raw pixels and audio to these structured representations, the system enables faster processing during classification while preserving the information needed for accurate segment identification.
3Manufacturing precision
If fingerprint data is used to identify program segments, then content management precision is improved, but data storage requirements increase
Solution Approach 1:
The system creates compact fingerprint representations of video segments instead of storing the actual video data. These fingerprints serve as efficient copies that capture the essential characteristics of each segment for identification and management purposes, dramatically reducing storage requirements while maintaining the precision needed for content management and segment classification.
Data Source
AI summary
In one aspect, an example method includes (i) obtaining fingerprint repetition data for a portion of video content, with the fingerprint repetition data including a list of other portions of video content matching the portion of video content and respective reference identifiers for the other portions of video content; (ii) identifying the portion of video content as a program segment rather than an advertisement segment based at least on a number of unique reference identifiers within the list of other portions of video content relative to a total number of reference identifiers within the list of other portions of video content; (iii) determining that the portion of video content corresponds to a program specified in an electronic program guide using a timestamp of the portion of video content; and (iv) storing an indication of the portion of video content in a data file for the program.