Convolutional Neural Network Video Annotation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for video content search on the internet face challenges due to the lack of meta-data for many video clips, with manual annotation being costly and inefficient for the rapid growth of online video content, and existing automated systems requiring labeled features which are time-consuming and not scalable.
Innovation Solution
The implementation of a system that uses a convolutional neuron network (CNN) to directly analyze video frames, generating annotations by applying deep learning architectures to pixel intensities without intermediate feature extraction, allowing for automatic derivation of meta-data from video frames and scalable annotation of video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to create meta-data for video clips, then annotation accuracy is improved, but productivity deteriorates due to high cost and inefficiency
Solution Approach 1:
The system enables videos to annotate themselves automatically through convolutional neural networks that process video frames and generate meta-data without human intervention. The network learns features directly from video content and performs classification to create annotations autonomously, eliminating the need for manual annotation while maintaining scalability.
Solution Approach 2:
The patent replaces the mechanical process of manual annotation with an automated computational system using convolutional neural networks. The CNN architecture processes video frames, extracts features, and generates annotations automatically, substituting human labor with an intelligent algorithmic system that operates at machine speed.
2Measurement precision
If existing automated annotation systems use labeled features, then measurement precision is improved, but productivity deteriorates due to time-consuming feature extraction
Solution Approach 1:
The system performs preliminary feature extraction and learning during the training phase of the convolutional neural network. The network pre-learns hierarchical features from training data, so during actual annotation, features are already extracted and ready for classification. This eliminates the need for time-consuming feature extraction during the annotation process itself.
Solution Approach 2:
The patent changes the parameters of the annotation system by using end-to-end learning where the convolutional neural network learns optimal feature extraction parameters automatically from data. Instead of using fixed, pre-defined feature parameters, the system adapts parameters through training to optimize both accuracy and speed for scalable annotation.
3Measurement precision
If deep learning architectures process all video frames at full resolution, then measurement precision is improved, but use of energy deteriorates due to computational complexity
Solution Approach 1:
The system segments the video processing task by selecting only representative frames (key frames) rather than processing all frames. The convolutional neural network processes these segmented frames to generate annotations, reducing the total computational load and energy consumption while maintaining annotation accuracy through strategic frame selection.
Solution Approach 2:
The patent applies partial action by processing only a subset of video frames that are most informative for annotation. Instead of exhaustively processing every frame at full resolution, the system selectively processes key frames that capture essential content, achieving sufficient annotation accuracy with reduced computational energy expenditure.
Data Source
AI summary
A method includes receiving, by a processing device of a content sharing platform, a video content, selecting at least one video frame from the video content, subsampling the at least one video frame to generate a first representation of the at least one video frame, selecting a sub-region of the at least one video frame to generate a second representation of the at least one video frame, and applying a convolutional neuron network to the first and second representations of the at least one video frame to generate an annotation for the video content.