Video Content Segmentation via Multi-Modal Feature Fusion
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Solution Overview
Problem
Current video content delivery systems lack the ability to efficiently segment and reconfigure video content based on visual, audio, and textual features, limiting user interaction and non-linear viewing experiences.
Innovation Solution
A system and method that analyze video content to extract visual, audio, and textual features, generating segments that can be characterized and categorized for linear or non-linear playback, allowing users to interact with specific clips or themes, and incorporating additional content such as commercials or commentary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video content is delivered as complete linear content, then content integrity is maintained, but user interaction and non-linear viewing capabilities are limited
Solution Approach 1:
The video content is divided into multiple segments based on detected visual, audio, and textual features. Each segment represents a meaningful portion of the content (e.g., scenes with specific objects, activities, or themes) that can be independently identified, stored, and retrieved, enabling non-linear viewing and interactive searching without requiring complete content re-delivery
Solution Approach 2:
The system performs preliminary analysis of video content to detect and tag segments with relevant features (objects, activities, themes) before user interaction. This advance segmentation and feature extraction creates an index structure that enables rapid retrieval and flexible reconfiguration of content segments based on user preferences
2Ease of operation
If video content is segmented and reconfigured for non-linear playback, then user experience and content relevance are enhanced, but system complexity increases
Solution Approach 1:
The segment detection system uses multi-modal feature analysis (visual, audio, and textual) to identify segments with multiple characteristic types. Each segment is tagged with multiple features (e.g., both visual objects and audio activities), allowing the same segmented structure to support various user queries and viewing preferences without requiring separate processing systems
Solution Approach 2:
The system introduces an intermediary layer of feature tags and metadata that bridges the raw video content and user interaction. These intermediate representations (detected features, segment boundaries, content characteristics) enable flexible content reconfiguration and retrieval without direct complex processing during user interaction
3Productivity
If complete video content is transmitted, then all content is available for viewing, but transmission time and bandwidth usage increase
Solution Approach 1:
The system extracts and transmits only the relevant video segments that match user preferences or search criteria, rather than transmitting complete video content. By extracting specific segments based on detected features and user queries, the system reduces transmission time and bandwidth consumption while delivering precisely the content users want to view
Data Source
AI summary
A method receives video content and metadata associated with video content. The method then extracts features of the video content based on the metadata. Portions of the visual, audio, and textual features are fused into composite features that include multiple features from the visual, audio, and textual features. A set of video segments of the video content is identified based on the composite features of the video content. Also, the segments may be identified based on a user query.