Multimodal Theme Classification via Knowledge Base Entity Linking
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Solution Overview
Problem
Existing methods for video theme classification, such as manual marking and single-modality machine learning, are inefficient, prone to errors, and fail to accurately classify videos in complex scenarios, especially when dealing with large multimedia databases.
Innovation Solution
A multimodality theme classification method that combines text and non-text information, including visual and audio features, using a pre-established knowledge base for entity linking and feature extraction to achieve more accurate theme classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual marking is used for video theme classification, then classification accuracy can be maintained, but efficiency and cost are significantly reduced
Solution Approach 1:
The patent replaces manual marking (mechanical human operation) with an automated machine learning system that processes video content through multiple modalities (visual, audio, text). The system automatically extracts features from video frames, audio signals, and subtitles, then classifies themes without human intervention, thereby maintaining accuracy while dramatically improving efficiency.
Solution Approach 2:
The system enables videos to classify themselves by automatically extracting and processing their own multimodal features. The video content (visual frames, audio, text) serves its own classification purpose through automated feature extraction and theme determination, eliminating the need for external manual marking while maintaining classification quality.
2Productivity
If single-modality machine learning is used for video classification, then processing speed is improved, but classification accuracy deteriorates in complex scenarios
Solution Approach 1:
The patent merges multiple modalities (visual information from video frames, audio information from sound tracks, and text information from subtitles) into a unified classification system. By combining features from all three modalities through feature fusion and joint processing, the system achieves both high processing speed and high classification accuracy in complex scenarios, overcoming the limitations of single-modality approaches.
3Device complexity
If traditional classification methods are used, then implementation simplicity is maintained, but adaptability to complex scenarios is reduced
Solution Approach 1:
The patent creates a universal classification system that handles multiple video scenarios (complex and simple) through a single multimodal framework. The system can process various video types (news, entertainment, education) and scenarios by adaptively weighting and fusing features from different modalities, making it versatile across diverse applications while maintaining a unified implementation architecture.
Data Source
AI summary
A theme classification method based on multimodality is related to a field of a knowledge map. The method includes obtaining text information and non-text information of an object to be classified. The non-text information includes at least one of visual information and audio information. The method also includes determining an entity set of the text information based on a pre-established knowledge base, and then extracting a text feature of the object based on the text information and the entity set. The method also includes determining a theme classification of the object based on the text feature and a non-text feature of the object.