Image Region Graph Clustering for Visual Organization
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Solution Overview
Problem
Existing systems fail to efficiently organize large corpora of digital images due to inconsistencies and inefficiencies in user-provided tag-based organization schemes, particularly when dealing with diverse sources and vast numbers of images.
Innovation Solution
The method involves representing image regions as vertices in a graph, connecting matching and overlapping regions with edges, assigning weights, and using graph clustering to automatically group images into visual clusters, leveraging image similarity for organization and search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user-provided tags are used to organize images, then organization is achieved, but scalability deteriorates when dealing with large corpora from diverse sources
Solution Approach 1:
The system performs automatic image grouping without requiring user intervention. The visual clustering module autonomously processes images, extracts features, builds graphs, and generates clusters, eliminating the need for manual tagging while maintaining organization quality across large corpora
Solution Approach 2:
The patent replaces the manual mechanical process of user tagging with an automated computational system. The visual clustering module uses computer vision algorithms, graph theory, and clustering techniques to substitute human effort with machine-based image analysis and automatic grouping
2Measurement precision
If manual tagging is performed for all images, then organization accuracy is improved, but user burden and time consumption increase significantly
Solution Approach 1:
The system automatically performs image analysis and grouping without requiring user time investment. The visual clustering module handles feature extraction, graph construction, and cluster generation autonomously, freeing users from time-consuming manual tagging tasks while maintaining consistent accuracy across all images
Solution Approach 2:
The system performs preliminary automatic grouping before user interaction. By pre-processing images through feature extraction and automatic clustering, the system prepares organized groups in advance, allowing users to review or refine results rather than creating tags from scratch, thus significantly reducing user time burden
3Stability of the object's composition
If consistent tagging is required from multiple users, then organization quality improves, but system complexity and coordination requirements increase
Solution Approach 1:
The system replaces multiple human taggers with a single automated visual clustering module that consistently applies the same algorithms to all images. This eliminates inter-user variability and coordination needs while maintaining stable, reproducible grouping results across the entire image corpus
Solution Approach 2:
The system transforms the subjective parameter of user interpretation into objective computational parameters through feature extraction. By converting images into feature vectors and using mathematical clustering algorithms, the system achieves consistent results based on quantifiable metrics rather than variable human judgment
Data Source
AI summary
Methods for grouping images from image corpora using graph clustering are presented. In one embodiment, a method is presented where grouping of images from a collection of digital images is done by: representing regions of images as vertices in a graph; connecting each pair of matching-vertices with a matching-edge; connecting each pair of overlap-vertices with an overlap-edge; assigning weights to each said matching-edge and to each said overlap-edge; clustering the graph, wherein clustering generates one or more vertex-clusters; and grouping the digital images into visual-clusters based on the vertex-clusters. Corresponding systems and computer program products are also presented.