Semantic Video Segmentation via ML Analysis
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Solution Overview
Problem
Current digital video streaming platforms lack the ability for users to easily segment and play specific sections of videos based on user-defined categories, requiring manual bookmarking and fast-forwarding, which is time-consuming and inefficient.
Innovation Solution
An auto-segmentation machine learning service that analyzes video attributes such as music, motion, and content to semantically segment videos into categories, allowing users to request and play back specific segments using natural language interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual bookmarking and fast-forwarding are used to locate specific video sections, then users can access desired content, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary segmentation of videos into semantically meaningful segments (scenes, shots, objects) before user playback. This pre-processing creates an indexed structure that enables instant retrieval of user-requested segments without manual navigation through the entire video
Solution Approach 2:
The patent introduces an intermediary semantic segmentation layer between the raw video content and user queries. This segmentation service acts as a mediator that translates user natural language requests into specific video segments, eliminating the need for direct manual navigation
2Productivity
If automated semantic segmentation is implemented to enable efficient video segment retrieval, then user experience is improved, but system complexity increases
Solution Approach 1:
The patent divides the video processing system into distinct modular components: a video segmentation service that breaks videos into semantic segments, a segment database for storage, and a playback system for retrieval. This modular segmentation allows each component to be independently optimized and managed
Solution Approach 2:
The semantic segmentation service acts as an intermediary layer between raw video data and user applications. This mediator handles the complexity of video analysis internally while presenting a simple interface to users, effectively hiding system complexity from end-users
3Measurement precision
If detailed semantic analysis of video attributes (music, motion, content) is performed to create accurate segments, then segmentation precision is improved, but processing time and computational resources increase
Solution Approach 1:
The video analysis is segmented into multiple independent attribute analyses (visual features, audio features, motion features) that can be processed in parallel. Each attribute is analyzed separately and then integrated to create comprehensive semantic segments, improving both accuracy and processing efficiency
Solution Approach 2:
The system performs comprehensive semantic analysis including visual, audio, and motion attributes to ensure high segmentation accuracy. This excessive analysis of multiple attributes guarantees precise segment boundaries and meaningful content classification, outweighing the additional processing time required
Data Source
AI summary
Systems and techniques are generally described for semantically segmenting videos. In various examples, a selection of a first video may be received. A first query to segment the first video into segments related to a first category of content may be received. A first plurality of segments related to the first category may be determined. In some examples, time code data representing the first plurality of segments may be sent to a remote computing device, wherein a video player of the remote computing device is effective to play the first plurality of segments based at least in part on the time code data.