Distributed Hierarchical Video Analysis for Efficient Semantic Labeling
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Solution Overview
Problem
Existing video labeling solutions face challenges in providing computationally efficient and accurate semantic labeling for large-scale video classification tasks, especially with the increasing volume of video content and the need for more specific labels, which traditional AI models struggle to address effectively.
Innovation Solution
A distributed hierarchical video analysis system that divides videos into basic units and uses a set of hierarchical classifier nodes to generate semantic tags and topics, with specialized nodes for audio and image data, optimizing computation and improving accuracy by routing units through specialized classifiers based on predefined logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional AI models are used for video labeling, then labeling can be performed, but computational efficiency deteriorates and scalability is limited for large-scale video classification tasks
Solution Approach 1:
The patent segments the video classification task into hierarchical levels (broad categories to specific topics) and divides computation across distributed worker nodes. Each worker handles specific classification operations for particular video segments or categories, enabling parallel processing and reducing the computational burden on any single node while improving overall labeling efficiency.
2Measurement precision
If more specific semantic labels are required for video classification, then labeling accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces a hierarchical dimension to the classification system, organizing labels from broad categories to specific topics across multiple levels. This hierarchical structure allows the system to achieve high labeling accuracy for specific topics while managing complexity by processing videos through progressively specialized classification stages rather than requiring all classifiers to operate simultaneously on all videos.
3Adaptability or versatility
If distributed processing is implemented, then scalability improves for large-scale video classification, but system coordination complexity increases
Solution Approach 1:
The patent designs worker nodes with universal functionality to perform multiple classification operations across different hierarchical levels and video segments. Each worker node can dynamically handle various classification tasks based on its capabilities and current system needs, reducing coordination complexity by having standardized, multi-functional units rather than requiring specialized nodes for each specific classification operation.
Data Source
AI summary
System and method of analyzing a video, comprising dividing the video into a set of successive basic units; generating semantic tags for the basic units using a set of hierarchical classifier nodes that comprise a parent classifier node and a plurality of child classifier nodes, wherein the basic units are each routed through selected child classifier nodes based on classification of the basic units by the parent classifier node; and generating a semantic topic for the video based on the semantic tags generated for the basic units.