Digital Image Clustering via Iterative Merging and Outlier Assignment
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Solution Overview
Problem
Current technologies lack an efficient method for automatically clustering digital images into semantically-related groups, which is essential for organizing large photo collections and improving search efficiency in visual search environments.
Innovation Solution
A method that involves determining similarity scores between images, clustering them into initial clusters, merging these clusters based on distances, and assigning unclustered images to the closest cluster or an outlier cluster, using a system with digital image processing circuitry and descriptor generators to facilitate dynamic clustering and efficient image organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image clustering is implemented, then image organization efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent divides the image set into multiple clusters iteratively, segmenting the large-scale image organization task into smaller, manageable groups. Each iteration processes a subset of images and creates one or more clusters, reducing the overall computational burden compared to processing all images simultaneously.
Solution Approach 2:
The patent performs preliminary actions by selecting representative images from each cluster to serve as reference points for subsequent clustering iterations. This preliminary selection simplifies future processing by using these representatives for similarity comparisons rather than comparing all images against all other images.
2Measurement precision
If similarity-based clustering is performed on all images, then clustering accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by computing similarity scores only between unclustered images and representative images from existing clusters, rather than computing all pairwise similarities. This partial computation maintains clustering accuracy for new images while significantly reducing processing time.
Solution Approach 2:
The patent segments the similarity computation task by dividing it into iterative batches where only relevant similarity comparisons are performed in each iteration, rather than computing the complete similarity matrix for all images at once.
3Reliability
If multiple clustering iterations are performed, then cluster quality is improved, but computational resources consumed increase
Solution Approach 1:
The patent implements dynamic clustering where the number of clusters and their compositions evolve iteratively based on similarity measurements. The system adaptively adjusts cluster structures across iterations, improving cluster quality while managing computational resources through progressive refinement rather than exhaustive processing.
Solution Approach 2:
The patent performs preliminary clustering actions in early iterations that establish a foundation for subsequent refinements. By creating initial clusters and selecting representatives beforehand, the system reduces the computational burden of later iterations while maintaining or improving cluster quality.
Data Source
AI summary
Digital image processing circuitry clusters a set of images into a set of first clusters of images and a set of unclustered images. The set of first clusters are merged, generating a set of second clusters of images. Images in the set of unclustered images are assigned to one of a cluster of the set of second clusters of images and an outlier image cluster. The clustered images may be partitioned into subclusters based on detection of objects in the images.