Video Database Generation Using Context, Audio, and Visual Fusion
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Solution Overview
Problem
Existing video database generation methods rely heavily on visual content analysis, leading to inaccurate feature correspondence and user experience issues due to the lack of consideration for contextual and audio information, resulting in inefficient retrieval and recommendation systems.
Innovation Solution
Integrate contextual, audio, and visual features to generate a video database, utilizing neural networks to determine contextual information, voice text, ambient sound, and image information, and fuse these features to create a comprehensive representation of video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If video database generation relies heavily on visual content analysis, then the processing complexity is reduced, but the accuracy of feature correspondence deteriorates
Solution Approach 1:
The patent merges visual features, audio features, and contextual features into a unified multi-modal feature representation. This combination allows the system to leverage multiple information sources simultaneously, improving feature correspondence accuracy while maintaining manageable processing complexity through integrated feature fusion mechanisms.
Solution Approach 2:
The patent implements a universal feature extraction framework that handles multiple modalities (visual, audio, contextual) through unified processing pipelines. This multi-functional approach enables the system to process diverse information types using consistent methods, improving accuracy without proportionally increasing complexity.
2Productivity
If only visual content is considered for video database generation, then the data processing efficiency is improved, but the user experience quality deteriorates
Solution Approach 1:
The patent extracts and processes audio features and contextual features in parallel with visual features during the initial video analysis phase. This preliminary multi-modal feature extraction ensures that all relevant information is prepared beforehand, enabling efficient subsequent processing while delivering high-quality user experience through comprehensive feature representation.
Solution Approach 2:
The patent introduces feature fusion mechanisms that act as intermediaries between different modalities (visual, audio, contextual). These fusion layers integrate multi-source information efficiently, maintaining processing speed while enhancing the quality and reliability of the generated video database for better user experience.
3Measurement precision
If multi-modal information is integrated for video database generation, then the retrieval accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent segments the multi-modal feature processing into distinct modules: visual feature extraction, audio feature extraction, contextual feature extraction, and feature fusion. This segmentation allows each component to be optimized independently, improving retrieval accuracy through comprehensive multi-modal analysis while controlling overall system complexity through modular architecture.
Data Source
AI summary
Illustrative embodiments of the disclosure include a method, device, and computer program product for generating a video database. The method includes determining a contextual feature indicating contextual information of a video. The method further includes determining, for a video frame in the video, an audio feature indicating voice text and an ambient sound associated with the video frame. The method further includes determining, for the video frame, a visual feature of the video frame. The method further includes generating a video database based on the contextual feature, the audio feature, and the visual feature. In this way, video features stored in the video database more accurately reflect the real meaning and contextual information of the video, and the generated video database can provide matching results that are more accurate and better conform with user demands for both a video retrieval system and a recommendation system, thus improving the user experience.