Video Summarization via Side Information Consensus
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Solution Overview
Problem
Conventional video summarization processes generate summaries independently from the video data itself, lacking a comprehensive knowledge base and often failing to provide the best representation of the original video content, as they ignore relationships across different videos.
Innovation Solution
A machine learning-based method for video summarization that leverages side information from related videos, including video data, still images, text, and natural language descriptions, to select important segments from the target video, using feature vectors and consensus estimation to generate a more informative summary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video summarization is performed independently using only the video data itself, then the summarization process is simple and fast, but the summary quality and representativeness are limited
Solution Approach 1:
The patent introduces side information from related videos as an intermediary element that mediates between the target video and the summarization process. This side information acts as a knowledge base that guides segment selection, improving summary quality without requiring complete reprocessing of all video data. The side information serves as a bridge that transfers relevant contextual knowledge from related videos to enhance the target video summary.
Solution Approach 2:
The patent performs preliminary processing by collecting and organizing side information from related videos before the actual summarization of the target video. This preliminary action involves gathering video data, still images, text, and natural language descriptions from related videos, and preparing this side information in advance to be used during the target video summarization process, thereby improving efficiency and quality.
2Loss of information
If side information from related videos is incorporated into the summarization process, then the knowledge base is expanded and summary quality improves, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential and relevant side information from related videos that is necessary for improving the target video summary. Instead of processing all data from related videos, the system selectively extracts useful video segments, still images, text, and natural language descriptions that contribute to better summary quality. This extraction principle reduces the volume of data to be processed while maintaining information completeness.
Solution Approach 2:
The patent applies partial action by using a selective subset of side information from related videos rather than incorporating all available data. The system processes only the portion of side information that is most relevant to the target video content, achieving sufficient information completeness without the full computational burden of processing excessive data.
3Adaptability or versatility
If multiple types of side information (video data, still images, text, natural language descriptions) are used, then the representativeness of the summary improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent implements a universal side information processing framework that handles multiple types of data (video data, still images, text, natural language descriptions) through a unified approach. The system processes diverse side information types using common processing mechanisms and integration methods, allowing the same system architecture to handle various information types without requiring separate specialized processing paths for each type.
Solution Approach 2:
The patent combines multiple types of side information (video data, still images, text, natural language descriptions) into a composite knowledge base that integrates heterogeneous data types. This composite structure allows the system to leverage the strengths of different information types together, creating a more comprehensive and representative summary while managing the complexity through unified processing approaches.
Data Source
AI summary
Machine learning-based techniques for summarizing collections of data such as image and video data leveraging side information obtained from related (e.g., video) data are provided. In one aspect, a method for video summarization includes: obtaining related videos having content related to a target video; and creating a summary of the target video using information provided by the target video and side information provided by the related videos to select portions of the target video to include in the summary. The side information can include video data, still image data, text, comments, natural language descriptions, and combinations thereof.