Video Edit Creation Using Recurrent Neural Network Segment Selection
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Solution Overview
Problem
Identifying and selecting segments of video content for inclusion in a video edit can be difficult and time-consuming due to the varying interest levels within video content.
Innovation Solution
A system that uses feature information and recommended feature information processed through a recurrent neural network to iteratively select video clips as segments of a video edit, where the selection is based on matches between the feature information of available clips and the recommended information for adjacent segments, allowing for dynamic and personalized video edit creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual identification and selection of video segments is performed, then the video edit can be customized according to user preferences, but the process becomes time-consuming and difficult
Solution Approach 1:
The system pre-processes video content by extracting feature information (visual, audio, textual) and storing it in a database before the actual editing task. This preliminary action allows the RNN-based model to quickly retrieve and match segments during video edit creation, significantly reducing the time required while maintaining customization capability
Solution Approach 2:
The patent replaces manual mechanical selection processes with an automated neural network system. The RNN model automatically identifies and selects video segments based on learned patterns from training data, substituting human judgment and manual operations with intelligent automated systems that operate faster and more consistently
2Productivity
If automated segment selection is implemented, then the time required for video edit creation is reduced, but the ability to maintain natural and varied content may be compromised
Solution Approach 1:
The system uses a recurrent neural network that processes feature information sequentially and generates predictions based on previous segment selections. This feedback mechanism allows the model to maintain context awareness and select segments that are not only efficient but also naturally coherent and varied, as each selection influences subsequent selections through the RNN's hidden state
Solution Approach 2:
The patent transforms qualitative aspects of video content (naturalness, variety, coherence) into quantifiable feature parameters that the neural network can process. By changing the representation of content characteristics into numerical features, the system can optimize for both speed and quality simultaneously through learned parameter relationships
Data Source
AI summary
Feature information characterize features of video clips may be obtained. A given video clip may be selected as a segment of a video edit. Other video clips may be iteratively selected as other segments of the video edit based on the feature information of the video clips and recommended feature information of the segments. Recommended feature information of a particular segment may be obtained by processing feature information of a previously selected video clip through a trained recurrent neural network. Video edit information defining the video edit may be generated. The video edit may include the selected video clips as the segments of the video edit.