Multi-Scale Video Content Classification via Temporal Segmentation
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Solution Overview
Problem
Existing video classification systems face challenges in detecting inappropriate content due to features with strong discriminative value occurring at multiple time scales and the periodicity and sparseness of time series features, making it difficult to accurately classify videos containing hate speech, violence, or adult content.
Innovation Solution
A method and system that generate multi-scale features from time-series features, allowing for the identification of inappropriate content by analyzing spatial or temporal portions of videos, using a combination of time-series feature generation, multi-scale feature generation, and inappropriate video classification modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-scale time series features are used for video classification, then the classification process is simple, but the accuracy is insufficient due to features with strong discriminative value occurring at multiple time scales
Solution Approach 1:
The patent segments the time series feature extraction process into multiple scales (e.g., different temporal windows or resolutions). Instead of using a single-scale feature extractor, the system employs multiple feature extractors operating at different scales, each capturing discriminative features at their appropriate temporal resolution. This segmentation allows the system to capture both short-term and long-term patterns in the video data, thereby improving classification accuracy while managing complexity through modular architecture.
2Measurement precision
If multiple time scales are analyzed to improve classification accuracy, then the discriminative value increases, but the computational complexity increases
Solution Approach 1:
The patent merges the outputs of multiple scale-specific feature extractors into a unified feature representation. By combining the discriminative features extracted at different scales into a single comprehensive feature vector, the system achieves high classification accuracy without requiring separate processing pipelines for each scale. This merging strategy reduces computational overhead compared to maintaining entirely independent processing streams for each scale.
Solution Approach 2:
The system dynamically adapts the scale of feature extraction based on the characteristics of the input video or the specific classification task. Rather than uniformly processing all videos at multiple fixed scales, the system can dynamically select appropriate scales or adjust the temporal resolution of feature extraction, thereby optimizing computational resource usage while maintaining high discriminative value for the features extracted.
3Adaptability or versatility
If traditional time series features are used, then the implementation is straightforward, but the system cannot handle periodicity and sparseness of features effectively
Solution Approach 1:
The patent changes the parameters of the feature extraction process to accommodate periodic and sparse features. This includes adjusting the temporal window size, sampling rate, or aggregation method based on the detected characteristics of the input signal. For periodic features, the system may synchronize extraction with the detected period, while for sparse features, it may use event-driven or adaptive sampling strategies. These parameter adjustments enable the system to effectively capture discriminative information from challenging feature patterns.
Data Source
AI summary
A method, a system and a computer program product generate a statistical classification model used by a computer system to determine whether a video contains content in a particular class, such as inappropriate content.