Video Feature Extraction via Multi-Typed Pooling
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Solution Overview
Problem
Current video processing technologies face challenges in efficiently and accurately comparing massive videos to identify duplicates, duplicates with copyright issues, and videos not suitable for display, due to the lack of effective methods for extracting and comparing video features.
Innovation Solution
A video feature extraction method that involves extracting frames from videos, processing them through multi-typed pooling processes (maximum, minimum, and average pooling) to obtain image features, and then determining video features by combining these features, with optional binarization for enhanced comparison efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video comparison methods are used, then video comparison can be performed, but the speed and accuracy of video comparison are insufficient
Solution Approach 1:
The video is segmented into multiple frames, and feature extraction is performed on individual frames rather than processing the entire video as a single unit. This allows for parallel processing of multiple frames and enables efficient comparison through frame-level feature extraction and aggregation.
Solution Approach 2:
The patent extracts key feature information from video frames by removing redundant data. Through multi-typed pooling processes, only essential features are retained and compressed into compact representations, enabling fast comparison while maintaining accuracy.
2Measurement precision
If comprehensive video features are extracted to improve comparison accuracy, then video comparison accuracy improves, but the complexity of feature extraction increases
Solution Approach 1:
Multiple pooling operations (max pooling, min pooling, average pooling) are combined in a unified feature extraction framework. These different pooling types capture diverse feature characteristics and are merged to form comprehensive video features, achieving high discrimination accuracy through integrated processing.
Solution Approach 2:
The feature extraction method uses multi-typed pooling processes that can handle various types of video content universally. The same pooling framework works across different video scenarios (duplicates, copyright violations, inappropriate content), providing a versatile solution that reduces the need for specialized extraction processes for different cases.
3Reliability
If video features are extracted in detail to ensure robustness, then feature robustness improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and pooling operations on video frames before final comparison. By pre-processing frames to extract and aggregate key features in advance, the system prepares robust feature representations that can be quickly compared later, reducing real-time processing requirements.
Solution Approach 2:
The pooling operations transform video frame data by changing parameters such as spatial dimensions and feature dimensions. Through max pooling, min pooling, and average pooling, the data is transformed into compact feature vectors with optimized dimensions, achieving robust representation while reducing processing complexity and time.
Data Source
AI summary
A video feature extraction method and device are provided. The method includes extracting one or more frames from a video object to obtain one or more frames of images; stage-by-stage processing each of the one or more frames of images by multi-typed pooling processes to obtain an image feature of the one or more frames of images; and determining a video feature according to the image feature of the one or more frames of images. The multi-typed pooling processes include a maximum pooling process, a minimum pooling process and an average pooling process.