AI Video Summarization Using Multimodal Feature Extraction
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Solution Overview
Problem
Existing video summarization technologies fail to effectively incorporate both video and text features to provide a comprehensive and intuitive summary of video content, leading to incomplete or redundant information.
Innovation Solution
An electronic apparatus and method that utilize a learning model based on AI algorithms to extract video and text features from video content, identify importance scores, and select key frames for a video summary, employing techniques like recurrent neural networks, histogram of oriented gradient, and text feature extraction methods to create a high-quality, iterative video summary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If only video features are used for video summarization, then the processing is simpler, but the summary completeness and accuracy deteriorates
Solution Approach 1:
The patent combines video features and text features into a unified feature set for frame importance scoring. The learning model integrates both modalities to comprehensively evaluate frame importance, ensuring that the video summary captures both visual and semantic information, thereby improving summary completeness without excessive complexity increase.
Solution Approach 2:
The learning model is designed to handle multiple types of features (video and text) universally. It processes diverse input features through a unified importance scoring mechanism, allowing the system to leverage both video and text information effectively while maintaining a manageable computational framework.
2Measurement precision
If a learning model is used to extract both video and text features, then the summary quality improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction by dividing video into frames and extracting both video and text features in advance. This preliminary processing allows the learning model to work with pre-extracted features rather than processing raw data in real-time, reducing the computational burden during the actual summarization process while maintaining high scoring accuracy.
Solution Approach 2:
The system extracts features from all video frames and transcript information, which may be more than strictly necessary. However, this comprehensive approach ensures that the learning model has sufficient data to accurately score frame importance, trading some processing time for improved scoring precision and summary quality.
3Reliability
If comprehensive feature extraction is performed on all video frames, then the summary accuracy improves, but the computational complexity increases
Solution Approach 1:
The video is segmented into individual frames, and the system processes each frame independently to extract video features. This segmentation allows the learning model to evaluate each frame's importance separately based on both video and text features, improving summary reliability while managing computational complexity through divide-and-conquer processing.
Solution Approach 2:
The system extracts only the essential features from each video frame and the transcript information that are relevant for determining frame importance. By selectively extracting and processing only the necessary features rather than all possible data, the system maintains high summary reliability while reducing unnecessary computational complexity.
Data Source
AI summary
The disclosure relates to an electronic apparatus, a method of controlling the same, and a computer program product, the electronic apparatus including: a memory configured to store an instruction; and a processor configured to execute the instruction to: extract video features of frames from a plurality of frames of video content, extract text features from transcript information of the video content, identify importance scores of the frames based on the video features and the text features, and select a key frame of the video content among the plurality of frames based on the identified importance scores. Thus, it is possible to provide faithful information about content to a user through the video summary considering not only the video features but also the text features.