Image Clustering via Feature Similarity Matrices
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Solution Overview
Problem
Current methods for organizing media content, such as images, lack efficiency in automatically clustering similar images without user intervention, often requiring manual categorization and failing to provide high-quality representative images.
Innovation Solution
A system utilizing a processor to detect common features in images, determine similarity matrices, and form clusters based on these features, with quality-based filtering to select representative images, allowing for automatic organization and presentation of image clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual categorization is used to organize images, then users can have control over organization, but user effort and time consumption increase significantly
Solution Approach 1:
The system performs automatic image clustering and quality-based filtering without requiring user intervention. The processor automatically detects features, forms clusters, and selects representative images, allowing the system to serve itself rather than requiring manual user categorization.
Solution Approach 2:
The patent replaces manual mechanical sorting operations with automated computational processing. Feature detection algorithms and clustering computations substitute for manual image organization, transforming the mechanical user action into an automated information processing task.
2Measurement precision
If all images are transmitted for processing, then complete analysis is achieved, but network resources and bandwidth are excessively consumed
Solution Approach 1:
The system extracts only the most representative images from each cluster for transmission and further processing. By selecting a subset of representative images rather than transmitting all images, the system reduces network bandwidth consumption while maintaining analysis quality.
Solution Approach 2:
Instead of processing all images equally, the system applies quality-based filtering to identify and process only the most relevant representative images. This partial action approach processes a smaller subset that provides sufficient information for effective organization.
3Productivity
If simple clustering methods are used, then processing speed is faster, but clustering accuracy and quality decrease
Solution Approach 1:
The system performs feature detection and similarity matrix computation as preliminary steps before final cluster formation. By preparing feature data in advance and using quality-based filtering to pre-select representative images, the system enables faster and more accurate clustering without requiring complex real-time processing.
Data Source
AI summary
A method that incorporates teachings of the subject disclosure may include, for example, determining, by a system comprising a processor, more common features of a plurality of images according to similarity matrices indicating relative similarities between instances of common features occurring within multiple images of the plurality of images, defining, by the system, cluster groups associated with the more common features, where each cluster group comprises cluster images of the plurality of images, and where the more common features are present in each the cluster images, and performing, by the system, quality-based filtering on the cluster images to identify a target cluster image to represent the cluster images for each of the cluster groups. Other embodiments are disclosed.