Image Clustering via Numeric Similarity Rules
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Solution Overview
Problem
The increasing number of photos captured and stored on user devices and in remote storage becomes overwhelming, as users face complexity in organizing and presenting images from various sources, especially when images are captured in rapid succession and from different devices or sources, leading to a need for improved organization and presentation methods.
Innovation Solution
A content management system that receives a plurality of images and applies similarity rules to categorize and cluster them, using numeric representations to group similar images, allowing for efficient organization and presentation within a user interface, even when images are from different sources or captured at different times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users store a large number of photos on devices and in remote storage, then the quantity of stored images increases, but the complexity of organizing and presenting images increases
Solution Approach 1:
The system automatically compares images using similarity rules and clusters them without requiring user intervention. The content management system performs the organization task autonomously by applying similarity rules to group images, eliminating the need for users to manually sort through large quantities of photos.
Solution Approach 2:
The system divides the large set of images into smaller clusters based on similarity. By segmenting the complete image collection into manageable groups of similar images, the system reduces the complexity of organizing and presenting the entire large quantity of photos.
2Quantity of substance
If users upload entire photo sets to content management systems, then the quantity of images is increased, but the ease of operation decreases
Solution Approach 1:
The content management system performs automatic organization by comparing uploaded images against each other and clustering them according to similarity rules. This self-service approach eliminates the need for users to manually organize large photo sets, maintaining ease of operation even when uploading numerous images.
Solution Approach 2:
The system performs preliminary comparison and clustering of images automatically upon upload. By pre-organizing images into clusters before the user needs to review them, the system reduces the operational burden on users when dealing with large quantities of uploaded photos.
3Productivity
If images are captured in rapid succession from multiple sources, then the quantity of images increases, but the difficulty of detecting and measuring similarities increases
Solution Approach 1:
The system replaces manual visual comparison with automated computational similarity rules. By substituting the mechanical process of manual image review with algorithmic comparison, the system can efficiently detect similarities among rapidly captured images from multiple sources without increasing difficulty.
Solution Approach 2:
The system transforms image comparison into quantitative parameter measurement using similarity rules that generate numeric results. By changing the approach from subjective visual assessment to objective parameter-based comparison, the system can handle high-volume image capture rates while maintaining consistent similarity detection.
Data Source
AI summary
Embodiments are provided for organization and presentation of content. In some embodiments, a plurality of images and a plurality of similarity rules for image categorization are received. For each image in the plurality of images, that image and each remaining image from the plurality is compared by: applying each similarity rule to the image and a remaining image from the plurality to obtain a numeric result, and recording the numeric result for the pair of images in a numeric representation, the numeric representation embodying similarities. The numeric representation is used as a reference for clustering the plurality of images into clusters of similar images, and each image is stored with a marker denoting a cluster to which it has been assigned.