Multi-Modal Video Feature Extraction and Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video processing methods lack accuracy in extracting feature information from videos, with a limited extraction range and failure to consider effective information, resulting in poor accuracy of extracted video features.
Innovation Solution
A video processing method that extracts multiple types of modal information (audio, text, and image) using preset feature extraction models and fuses these features to obtain a multi-modal feature, expanding the extraction range and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple feature extraction is used, then processing speed is fast, but accuracy of extracted video feature information is poor
Solution Approach 1:
The video feature extraction process is segmented into multiple independent modules: audio feature extraction, text feature extraction, and video feature extraction. Each module processes a specific modality separately using dedicated algorithms, then the results are fused to produce comprehensive video features. This segmentation allows each module to be optimized for its specific task while maintaining overall system accuracy.
Solution Approach 2:
The patent transitions from single-modal feature extraction to multi-modal feature extraction by adding new dimensions of information processing. Instead of only analyzing video frames, the system simultaneously processes audio signals, text content, and video visual information, creating a multi-dimensional feature space that significantly improves extraction accuracy.
2Adaptability or versatility
If single-modal feature extraction is used, then processing is simple, but extraction range is small and effective information is not considered
Solution Approach 1:
The system is designed with multi-functional capability to handle multiple types of information modalities (audio, text, video) within a single processing framework. Each modality has its own specialized extraction algorithms, but they all feed into a unified feature fusion module that produces comprehensive video features, making the system versatile across different information types.
Solution Approach 2:
The patent merges multiple feature extraction processes (audio feature extraction, text feature extraction, video feature extraction) into a unified multi-modal feature extraction system. The features from different modalities are combined and fused to create comprehensive video features, expanding the extraction range to include all effective information from various sources.
3Measurement precision
If comprehensive multi-modal feature extraction is used, then accuracy is improved, but processing complexity increases
Solution Approach 1:
The system performs preliminary processing on each modality independently before fusion: audio signals are pre-processed for feature extraction, text is segmented and analyzed, and video frames are pre-processed. This preliminary action on individual modalities simplifies the subsequent fusion process and improves overall processing efficiency by avoiding redundant computations.
Solution Approach 2:
The patent uses separate processing pipelines for each modality (audio, text, video), creating parallel copying of the extraction process for each type of information. Each pipeline is optimized for its specific modality, and the results are then fused, allowing efficient processing while maintaining high accuracy for each information type.
Data Source
Figure 1~2
Figure 3~6
Figure 7~8
AI summary
The present description provides a video processing method and apparatus. The video processing method comprises: extracting at least two types of modal information from a received target video; extracting, according to a preset feature extraction model, at least two types of modal features corresponding to the at least two types of modal information; and fusing the at least two types of modal features to obtain a target feature of the target video, so as to facilitate further application by a subsequent user based on the target feature.