Video Ontology Database Construction via Node Relation Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional video databases lack the ability to reflect mutual relations between video data, limiting their capacity to efficiently classify and manage video data, search for similar content, and provide supplementary services like rights management and advertising.
Innovation Solution
A method and system that generate nodes and node information based on mutual relations between video data, including identifying identical, completely identical, and overlapping data, to construct a video ontology database that can classify and manage video data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional video databases store only individual video data information, then the database structure remains simple, but the ability to reflect mutual relations between video data is lost
Solution Approach 1:
The database is segmented into multiple tables including video data tables for storing individual video information and relation tables for storing mutual relations between videos. This segmentation allows the system to maintain simple storage of individual video data while separately capturing the complex relational structures through dedicated relation tables that link video data using unique identifiers.
2Productivity
If the database stores detailed mutual relations between all video data, then classification and management efficiency improves, but the database construction complexity increases
Solution Approach 1:
The system performs preliminary classification of video data relations into predefined categories such as identicalness, similarity, and other relational types. By establishing this classification framework in advance through relation tables with predefined relation types, the database can efficiently manage and classify video data without requiring complex real-time analysis during operations.
3Adaptability or versatility
If the database constructs comprehensive mutual relations between video data, then services like searching and rights management improve, but the time and resources required for database construction increase
Solution Approach 1:
The system implements a multi-level relation construction approach where essential relations (such as identicalness and primary similarity) are established first to enable core services like searching and basic rights management. Additional detailed relations can be constructed progressively as needed, allowing the database to become functional with partial relation data while maintaining the capability to add comprehensive relations over time.
Data Source
AI summary
The present invention relates to a method and system for constructing a database (DB) based on mutual relations between pieces of video data. The present invention provides the method of constructing a DB based on mutual relations between pieces of video data, including 1) generating one or more nodes so that pieces of identical video data are included in an identical node, 2) generating pieces of node information about respective generated nodes, 3) comparing comparison target video data with pieces of video data of the respective nodes, and then setting relations between the comparison target video data and the pieces of video data of the respective nodes, and 4) updating pieces of node information about the respective nodes, based on the set relations, and also provides a DB construction system using the method.