Hierarchical Clustering for Media Compilation Generation
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Solution Overview
Problem
Manually sorting through a user's media library to create media compilations is difficult and time-consuming due to the large number of media items, such as images and videos, which requires efficient organization methods.
Innovation Solution
A system that iteratively clusters media items into a hierarchy of collections based on proximity of capture times and locations, allowing for the creation of media compilations by identifying suitable collections for inclusion based on size, capacity, and diversity, using machine-readable instructions and computer program components to process and organize media items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual sorting is used to create media compilations, then user control and customization are improved, but time consumption and difficulty increase
Solution Approach 1:
The system performs automatic hierarchical clustering of media items using capture time and location data without requiring manual user intervention. The algorithm independently organizes media into scenes, collections, and higher-level groups, allowing the system to serve itself in creating compilations rather than relying on manual sorting by users.
Solution Approach 2:
The system transforms media organization from manual category-based sorting to automatic clustering based on temporal and spatial parameters. By utilizing capture time and capture location metadata, the system dynamically groups media items according to their inherent chronological and geographical relationships, resolving the contradiction between ease of operation and time consumption.
2Manufacturing precision
If manual sorting is used to organize media items, then precise control over compilation content is improved, but complexity and time consumption increase
Solution Approach 1:
The organization process is segmented into hierarchical levels: individual media items are first grouped into scenes based on temporal proximity, then scenes are grouped into collections based on spatial proximity, and finally collections are organized into higher-level groups. This segmentation reduces complexity by breaking down the overwhelming task of organizing all media items at once into manageable hierarchical stages while maintaining precise control over compilation content.
Solution Approach 2:
The system introduces intermediate organizational structures (scenes and collections) as mediators between individual media items and final compilations. Scenes act as intermediaries grouping items by time, collections group scenes by location, and these intermediate layers enable precise content control without requiring direct manual sorting of all individual items, thus reducing organizational complexity.
3Reliability
If all media items are processed together, then comprehensive organization is improved, but computational complexity and processing time increase
Solution Approach 1:
The computational process is segmented into multiple passes: first clustering media items into scenes based on capture time, then clustering scenes into collections based on capture location, and finally organizing collections into higher-level groups. This segmentation allows comprehensive organization of all media items while reducing computational complexity at each stage by working with progressively smaller and more organized data sets rather than processing all items simultaneously.
Solution Approach 2:
The system performs preliminary clustering actions in a hierarchical sequence, first organizing media items into scenes based on temporal data, then into collections based on spatial data, before final compilation creation. These preliminary organizational actions reduce the complexity of subsequent processing steps by pre-grouping data, ensuring comprehensive organization while managing computational complexity through staged processing.
Data Source
AI summary
Media items may be obtained. The media items may be characterized by capture information indicating a capture time and a capture location of individual media items. The media items may be clustered into scenes based on proximity of the capture times of the media items. The scenes may be clustered into collections based on proximity of the capture times and/or the capture locations of the media items within the scenes. The collections may be iteratively clustered into higher collections based on proximity of the capture times and/or the capture locations of the media items within the collections. One or more collections may be identified for inclusion in a media compilation based on a size of the identified collection(s). A media compilation, including one or more of the media items included in the identified collection(s), may be generated.