Video Summarization Using Semantic Feature Extraction
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Solution Overview
Problem
Existing video summarization technologies rely on low-level features like color histograms and optical flow, which are content-agnostic and ineffective in capturing semantically meaningful moments, especially in unedited videos captured by mobile or wearable devices, limiting their applicability to single-event videos.
Innovation Solution
The use of convolutional neural networks (CNNs) to extract deep features from videos, identifying high-level semantic contexts such as activities, objects, and locations, and implementing an importance scoring mechanism to generate a content-aware summary that is invariant to camera motion and illumination changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-level features like color histograms and optical flow are used for video summarization, then the system is simple to implement, but it cannot capture semantically meaningful moments and is ineffective for unedited videos
Solution Approach 1:
The patent replaces traditional mechanical feature extraction methods (color histograms, optical flow) with deep learning-based semantic feature extraction. Convolutional neural networks automatically learn hierarchical features from video frames, capturing semantically meaningful information about activities, objects, and scenes without manual feature engineering.
Solution Approach 2:
The patent transforms the feature representation parameters from low-level visual features to high-level semantic features. By changing the parameter space from pixel-level statistics to semantic concept vectors, the system achieves better accuracy in identifying representative moments while maintaining computational feasibility through efficient neural network architectures.
2Measurement precision
If deep features extracted by convolutional neural networks are used, then semantic meaning is captured effectively, but the computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the essential semantic features needed for summarization rather than processing all video data. By identifying and extracting key semantic contexts (activities, objects, locations) that are most relevant for representation, the system reduces processing time while maintaining high accuracy in capturing meaningful moments.
Solution Approach 2:
The patent applies partial processing by focusing computational resources on key frames and segments that contain the most important semantic information. Rather than uniformly processing all video content, the system identifies and processes only the portions necessary for creating an accurate summary, reducing overall processing time.
3Measurement precision
If comprehensive semantic analysis is performed on all video content, then accurate summaries are generated, but storage and communication bandwidth are consumed by processing and transmitting large amounts of data
Solution Approach 1:
The patent creates a compressed semantic representation (copy) of the video content rather than storing or transmitting the original video data. By encoding video information as condensed semantic features and summary metrics, the system achieves accurate video summaries while dramatically reducing storage requirements and communication bandwidth consumption.
Data Source
AI summary
Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.