Multi-Modal Video Classification via Image Audio Text Fusion
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Solution Overview
Problem
Existing video classification methods based solely on image features are inadequate for accurately distinguishing videos with similar images but differing significantly in audio or text, leading to poor classification results.
Innovation Solution
A video classification method that combines image, audio, and textual features using respective classification models to determine a target classification result, incorporating image classification results from a first model, audio classification results from a second model, and textual classification results from a third model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video classification is performed based solely on image features, then the classification process is simple and fast, but the classification accuracy deteriorates when videos have similar images but different audio or text
Solution Approach 1:
The patent segments the video classification task into three independent classification processes: image classification, audio classification, and text classification. Each modality is processed separately by its own classification model, and the results are subsequently integrated. This segmentation allows each model to specialize in its modality while maintaining overall system efficiency.
Solution Approach 2:
The patent combines multiple classification results (image, audio, text) into a composite classification outcome. By integrating features from different modalities and their respective classification results, the system creates a more robust and accurate video classification that leverages the complementary strengths of each modality.
2Measurement precision
If multiple classification models (image, audio, text) are combined to classify videos, then classification accuracy improves, but the system complexity increases
Solution Approach 1:
The system divides the complex multi-modal classification task into three separate, manageable classification models (image classifier, audio classifier, text classifier). Each model operates independently on its respective modality, reducing the complexity of individual models while achieving superior overall accuracy through their integration.
Solution Approach 2:
The patent employs a unified framework that processes multiple modalities (image, audio, text) through a common integration mechanism. This multi-functional approach allows the system to handle different types of data uniformly, managing complexity through a standardized processing pipeline that accommodates diverse input types.
3Loss of time
If only image features are used for video classification, then the processing time is reduced, but the ability to distinguish videos with similar images but different content deteriorates
Solution Approach 1:
The patent segments the video analysis into parallel processing streams for image, audio, and text modalities. By processing these segments concurrently rather than sequentially, the system minimizes total processing time while ensuring that each modality contributes to the final classification, thereby improving content discrimination capability.
Solution Approach 2:
The system performs classification on all available modalities (image, audio, text) rather than selecting only the most critical ones. This excessive action ensures that no discriminative information is missed, even though it increases processing requirements, thereby maximizing content discrimination accuracy.
Data Source
AI summary
Video classification accuracy can be improved by utilizing multiple features. Classification based on a combination of an image classification model, an audio classification model, and a textual description classification model may improve classification. The image classification result is based on an image feature of the image frame. The audio classification result is based on an audio feature of the audio. The textual description classification result is based on a text feature of the textual description information. A target classification result of the target video is determined based on to the image classification result, the audio classification result, and the textual classification result.