Dynamic Video Comparison Summaries for Multi-Topic Queries
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Solution Overview
Problem
Existing video summarization technologies are unable to dynamically generate video comparison summaries in real-time for multiple specific topics that are not previously stored in a video repository, limiting user navigation and engagement.
Innovation Solution
Implementing a video comparison program using machine learning techniques, including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Generative Adversarial Networks (GANs) to analyze and generate intra-frame or inter-frame video comparisons based on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video summarization technologies are used to generate summaries from existing video repositories, then navigation of large volumes of video data is improved, but the ability to search for comparisons of multiple topics that do not exist in the repository is lost
Solution Approach 1:
The system dynamically generates video comparison summaries on-demand based on user queries, transitioning from static pre-stored summaries to dynamic real-time generation. This allows the system to adapt to any comparison topic without requiring pre-existing video content in the repository.
Solution Approach 2:
The system introduces an intermediary comparison generation module that synthesizes new video comparisons by combining relevant segments from multiple source videos. This intermediary process enables comparison searches for topics that don't exist as complete videos in the repository.
2Loss of information
If deep-learning-based video summarization is used to produce static or dynamic summaries, then informative video synopses are generated, but real-time dynamic comparison generation for multiple topics is not achieved
Solution Approach 1:
The system segments video content into topic-specific clips and uses separate deep-learning models to process each segment independently. This segmentation allows parallel processing of multiple video sources, reducing overall generation time while maintaining information quality.
Solution Approach 2:
The system performs preliminary actions by pre-processing and indexing video content into topic-based segments before comparison is needed. This advance preparation enables faster real-time comparison generation when user queries are received.
3Reliability
If traditional video retrieval methods are used to find videos on specific topics, then existing video content is located, but dynamic generation of new video comparisons is not possible
Solution Approach 1:
The system performs self-service by automatically generating video comparisons without human intervention. The deep-learning models autonomously select relevant video segments, synthesize comparisons, and output results, enabling full automation of the video comparison generation process.
Solution Approach 2:
The system achieves universality by combining multiple functions: video retrieval, topic classification, segment selection, and comparison synthesis into a single multi-functional platform. This allows the system to handle both exact match retrieval and dynamic comparison generation through the same infrastructure.
Data Source
AI summary
A method, computer system, and a computer program product for dynamically generated video comparison summary is provided. The present invention may include receiving a query for a video comparing a plurality of topics included in the query. The present invention may then include identifying a plurality of video content relevant to the plurality of topics. The present invention may next include mapping at least one video content of the plurality of video content to respective topics of the plurality of topics. The present invention may further include predicting whether to generate an intra-frame comparison video or an inter-frame comparison video for the video comparing the plurality of topics included in the query. The present invention may then include generating the video comparing the plurality of topics included in the query based on the prediction.