Adaptive Video Frame Sampling for Content Classification
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Solution Overview
Problem
The immense volume of online video content necessitates efficient automated classification methods to quickly identify content types, such as NSFW material or celebrity faces, to improve searchability and manageability, as human review is slower and less efficient.
Innovation Solution
Adaptive content classification systems that adjust the sampling rate of frames analyzed by content classifiers based on previous frame analysis and combine multiple frames into collages for concurrent classification, allowing for faster and more accurate identification of content types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frames are analyzed at a high sampling rate to improve classification accuracy, then measurement precision is improved, but productivity deteriorates due to increased processing time
Solution Approach 1:
The patent applies dynamics by making the frame sampling rate adaptive rather than fixed. The system dynamically adjusts the sampling rate based on content characteristics detected in previously analyzed frames. When sensitive content is detected, the system increases the sampling rate to ensure accurate classification. When no sensitive content is present, the system decreases the sampling rate to improve processing throughput. This dynamic adjustment resolves the contradiction between maintaining high classification accuracy and achieving high processing productivity.
Solution Approach 2:
The patent changes the parameter of frame sampling rate based on the analysis results of previous frames. The system monitors content characteristics and adjusts the sampling interval accordingly. This parameter change allows the system to optimize the balance between classification precision and processing speed, improving productivity without sacrificing accuracy when sensitive content is present.
2Measurement precision
If all frames are analyzed to ensure accurate content classification, then measurement precision is improved, but loss of time increases due to processing the entire video sequence
Solution Approach 1:
The patent applies partial action by analyzing only a subset of frames rather than all frames in the video sequence. The system uses adaptive sampling to select which frames to analyze based on content characteristics. When sensitive content is detected in sampled frames, the system performs comprehensive analysis to ensure accuracy. When no sensitive content is detected, the system reduces analysis to the sampled subset, thereby reducing processing time while maintaining sufficient classification accuracy.
Solution Approach 2:
The system performs preliminary analysis on sampled frames to detect content characteristics before deciding whether to analyze additional frames. This preliminary action allows the system to identify sensitive content early and adjust the sampling rate accordingly, avoiding the need to analyze every frame while maintaining classification accuracy when sensitive content is present.
3Measurement precision
If the sampling rate is increased to process sensitive content more accurately, then measurement precision is improved, but productivity deteriorates due to slower overall processing
Solution Approach 1:
The system dynamically adjusts the sampling rate based on the detected need for sensitive content detection. When sensitive content is identified in sampled frames, the system increases the sampling rate to ensure accurate classification. When no sensitive content is present, the system decreases the sampling rate to maintain high processing throughput. This dynamic adaptation resolves the contradiction between precision and productivity.
Solution Approach 2:
The system uses feedback from the analysis of previously sampled frames to adjust the sampling rate for subsequent frames. When sensitive content is detected in the feedback from previous frame analyses, the system responds by increasing the sampling rate. This feedback mechanism ensures that high precision is applied only when necessary, thereby maintaining overall productivity while ensuring accurate sensitive content detection.
Data Source
AI summary
In a method for performing adaptive content classification of a video content item, frames of a video content item are analyzed at a sampling rate for a type of content, wherein the sampling rate dictates a frequency at which frames of the video content item are analyzed. Responsive to identifying content within at least one frame indicative of the type of content, the sampling rate of the frames is increased. Responsive to not identifying content within at least one frame indicative of the type of content, the sampling rate of the frames is decreased. It is determined whether the video content item includes the type of content based on the analyzing the frames.