Automated Video Editing via Semantic Data Analysis
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Solution Overview
Problem
Traditional video editing solutions are complex and time-consuming for consumers, requiring significant interaction and not providing a feasible alternative for producing a professional-looking video without the cost of professional services.
Innovation Solution
A method and system for automated video editing that extracts semantic data, identifies pre-defined events based on matched characteristics, assigns matching and theme relevancy values, and applies corresponding editing effects, reducing consumer interaction and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional video editing solutions are used, then video editing can be performed, but the process becomes complex and time-consuming requiring significant consumer interaction
Solution Approach 1:
The system automatically analyzes video content, extracts semantic data, identifies events and objects, and applies editing decisions without requiring consumer interaction. The editing system serves itself by making intelligent decisions based on semantic analysis of the video content, eliminating the need for manual editing instructions from the user.
Solution Approach 2:
The system performs preliminary semantic analysis of the video content before editing begins, extracting meaningful data about events, objects, and relationships. This preliminary understanding of the video content enables automated editing decisions to be made based on pre-identified semantic structures rather than requiring real-time user input during the editing process.
2Loss of time
If automated video editing is implemented, then consumer interaction is reduced, but the system must analyze and interpret semantic data to make accurate editing decisions
Solution Approach 1:
The system introduces semantic data as an intermediary layer between the raw video content and the editing decisions. Semantic data extraction modules analyze video content and transform it into structured semantic representations that can be easily processed by editing algorithms, simplifying the overall analysis task while enabling automated editing.
Solution Approach 2:
The video editing process is divided into separate functional modules: semantic data extraction, event identification, object recognition, and editing decision-making. Each module handles a specific aspect of the analysis, breaking down the complex task of semantic understanding into manageable segments that can be processed independently and efficiently.
Data Source
AI summary
Systems and methods for video editing are described. At least one embodiment includes a method for editing a video comprising: extracting semantic data from the video, searching the extracted semantic data to identify characteristics that match a pre-defined set of characteristics, identifying a pre-defined event based on the matched characteristics within the semantic data, assigning a matching degree value to the event, assigning a theme relevancy value to the event, and automatically editing the event based on the identified event, the assigned matching degree value, and the theme relevancy value.