Video Retrieval Using Semantic Paragraph Correlation
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Solution Overview
Problem
Existing video retrieval systems face challenges in accurately retrieving videos without predefined content tags, as they lack extensibility and often result in redundant search results due to repeated tags, making it difficult to handle natural language queries effectively.
Innovation Solution
A method and device for video processing that determines preselected videos based on paragraph information and video information, and identifies a target video using video frame and sentence information, allowing for accurate retrieval without relying on predefined tags, thereby reducing redundancy and enabling natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content tags are used for video retrieval, then retrieval can be performed through predefined categories, but the system lacks extensibility and cannot handle videos without tags or natural language queries
Solution Approach 1:
The patent replaces the manual mechanical process of tag definition and classification with an automated semantic analysis system. The system automatically extracts entities, relationships, and events from video content and queries, eliminating the need for manual tag creation and enabling natural language queries without predefined tags.
Solution Approach 2:
The patent creates a universal retrieval system that handles multiple query types (natural language, tagged videos, untagged videos) through a single semantic analysis framework. The knowledge graph and entity relationship analysis provide a unified approach that works across different video types and query formats, eliminating the need for separate retrieval mechanisms.
2Measurement precision
If content tags are used for video retrieval, then videos can be categorized, but repeated tags across different videos result in redundant retrieval results
Solution Approach 1:
The patent introduces an intermediary semantic analysis layer that processes both video content and queries before matching. This intermediary layer extracts meaningful entities and relationships, then uses knowledge graph reasoning to establish precise connections between queries and videos, filtering out redundant results while maintaining comprehensive coverage.
Solution Approach 2:
The patent segments the retrieval process into distinct stages: query semantic analysis, video semantic analysis, knowledge graph matching, and result filtering. This segmentation allows each stage to focus on specific aspects of the retrieval task, improving accuracy while reducing redundancy through systematic processing.
3Ease of operation
If traditional tag-based retrieval is used, then the system is simple to implement, but it cannot process natural language queries effectively
Solution Approach 1:
The patent replaces the simple but limited tag-matching mechanism with an automated semantic analysis system that processes natural language. The system automatically performs entity recognition, relationship extraction, and event detection on user queries, enabling effective retrieval without requiring users to learn tag systems or input complex search terms.
Data Source
AI summary
The present disclosure relates to a method and device for video processing, an electronic device, and a storage medium. The method comprises: determining, on the basis of paragraph information of a query text paragraph and video information of multiple videos in a video library, preselected videos associated with the query text paragraph in the multiple videos; and determining a target video in the preselected videos on the basis of video frame information of the preselected videos and of sentence information of the query text paragraph. The method for video processing of the embodiments of the present disclosure indexes videos by means of the relevance between the videos and the query text paragraph, allows the pinpointing of the target video, avoids search result redundancy, allows the processing of the query text paragraph in a natural language form, and is not limited by the inherent contents of content labels.