Content Summarization via Cluster Diffusion for Mobile Devices
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Solution Overview
Problem
Conventional content summarization techniques prioritize quality and motion, leading to inadequate coverage of input content and are computationally intensive, making them unsuitable for mobile devices.
Innovation Solution
The method involves clustering content segments based on similarity, calculating diffusion values to select representative segments, and including them in a summary that meets a predetermined threshold, effectively improving content coverage and being computationally feasible for mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional summarization techniques prioritize quality and motion selection, then higher quality segments are selected, but content coverage becomes inadequate
Solution Approach 1:
The input media is divided into multiple segments that are then clustered into groups. By selecting representative segments from each cluster rather than individually selecting high-quality segments, the method ensures broader content coverage while maintaining quality. The segmentation allows the system to capture diverse content types across the entire media duration.
Solution Approach 2:
The patent uses computationally efficient algorithms that can be executed on mobile devices with limited resources. The summarization process employs lightweight clustering and selection methods that sacrifice some computational intensity to achieve practical deployment on consumer devices, enabling local processing without requiring powerful servers.
2Manufacturing precision
If conventional summarization techniques use comprehensive analysis, then better content selection is achieved, but computational complexity increases
Solution Approach 1:
By segmenting the media into clusters and selecting representatives from each cluster, the computational problem is divided into smaller, more manageable sub-problems. This approach reduces the overall complexity compared to analyzing all segments individually while maintaining selection accuracy through the clustering structure.
Solution Approach 2:
The patent transforms the complex selection problem into a simpler clustering problem with clear parameters. By defining clusters based on similarity metrics and selecting representatives based on cluster properties rather than individual segment quality metrics, the computational requirements are reduced while maintaining effective content selection.
3Ease of operation
If summarization is performed on mobile devices, then user convenience is improved, but processing power is insufficient
Solution Approach 1:
The patent changes the computational parameters from intensive individual segment analysis to lighter clustering-based selection. This parameter transformation enables mobile devices with limited processing power to perform summarization locally, improving user convenience by eliminating the need for cloud processing or external computing resources.
Solution Approach 2:
The mobile device performs the summarization processing itself using the efficient clustering algorithm, rather than requiring external server assistance. This self-service capability enables offline operation and protects user privacy while working within the computational constraints of mobile hardware.
Data Source
AI summary
The disclosed technology includes techniques for improved content coverage in automatically-generated content summaries. The technique may include clustering a set of input content, determining diffusion for each cluster, and selecting representatives of each cluster to optimize other secondary metrics. Various types of input content may be used, including groups of images, video clips, or other multimedia content. Contiguous content may be manually or programmatically divided into discrete portions before clustering, for example, a lengthy video divided into a number of short clips. In some implementations, the disclosed technique may be implemented effectively on a mobile device. In other words, the processing required may be computationally feasible for execution on a smartphone or similar device.