Video Categorization via Face Feature Matching
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Solution Overview
Problem
Current multimedia technologies lack an efficient and intelligent method for automatically categorizing videos based on the presence of a specific person, relying on manual user intervention which is inefficient and lacks accuracy.
Innovation Solution
A video categorization method and apparatus that acquires a key frame with a face, extracts face features, and matches them with pre-defined picture categories to automatically assign videos to corresponding categories, utilizing face recognition technology and clustering algorithms to determine the appropriate category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual video categorization is used, then users can categorize videos, but the process is inefficient and lacks intelligent technology
Solution Approach 1:
The system enables videos to automatically categorize themselves by extracting face features and matching them with existing picture categories without requiring user intervention. The video processing unit autonomously performs face detection, feature extraction, and category assignment based on similarity comparison with stored picture face features.
2Adaptability or versatility
If face clustering technology is used for photos, then photos with the same person can be categorized, but this technology is not available for videos
Solution Approach 1:
The patent extends face clustering technology from photos to videos by using the same face feature extraction and matching algorithms. The video processing unit extracts face features from video frames and compares them with picture category face features, making the categorization technology universally applicable to both photos and videos.
3Extent of automation
If automatic video categorization is implemented, then intelligent technology is provided, but the system complexity increases
Solution Approach 1:
The system extracts only the essential face features from videos for categorization purposes, rather than processing the entire video content. By focusing on face feature extraction and matching, the system achieves intelligent categorization while avoiding the complexity of analyzing all video elements.
Data Source
AI summary
A video may be categorized into a picture category or a video category. A key frame of the video includes a face and a face feature in the key frame is obtained. Face features respectively associated with a plurality of picture categories are acquired and the video is assigned to one of the picture categories based on a comparison of the key frame face feature and the face features of the picture categories. Videos may first be associated with a video category by comparing key frame face features from the videos, and then the video category may be assigned to a picture category based on comparison of a video category face feature with a plurality of picture category face features. Alternatively, a video may be assigned to a picture category based on matching capture times and capture locations between the video and a reference picture in the picture category.