Image Clustering via Similarity Distance for Efficient Organization
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Solution Overview
Problem
The rapid growth in image data from various sources makes it challenging for users to organize, manage, and navigate large collections of images effectively, leading to time-consuming and tedious processes of categorization and retrieval.
Innovation Solution
A computer-implemented method that allocates images into groups based on similarity distance, using image-specific allocation criteria such as pixel content comparison, and displays representative images to facilitate organization and visualization, allowing for automatic allocation and hierarchical structuring of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If images are manually organized and categorized by users, then images can be properly organized and retrieved, but the process becomes time-consuming and tedious especially with large volumes of images
Solution Approach 1:
The system enables images to organize themselves automatically through content-based clustering algorithms. The computer processor analyzes image content, determines allocation criteria, and groups images without human intervention, allowing the image collection to self-organize based on visual similarity and metadata
Solution Approach 2:
The patent replaces the mechanical manual sorting process with an automated computational system. Instead of users physically dragging and dropping images into folders, a computer processor executes algorithms that automatically allocate images to groups based on content analysis, substituting human manual labor with automated image processing and clustering techniques
2Ease of operation
If all images in a large collection are displayed for navigation, then users can identify specific images, but the process becomes overwhelming and difficult to navigate
Solution Approach 1:
The patent segments the large image collection into multiple clusters based on content similarity. Instead of presenting all images at once, the system divides them into manageable groups (e.g., landscapes, portraits, objects) and displays representative images for each cluster, allowing users to navigate through organized segments rather than a monolithic overwhelming list
Solution Approach 2:
The patent introduces a new organizational dimension by grouping images based on visual content similarity rather than traditional flat folder structures or chronological order. This creates a semantic dimension for navigation where images are organized by their visual characteristics, adding a meaningful layer of structure that simplifies user exploration of large collections
3Adaptability or versatility
If traditional folder-based organization is used, then images can be categorized, but the system cannot effectively handle the volume and variety of modern image collections
Solution Approach 1:
The patent changes the organizational parameters from traditional metadata-based sorting (dates, locations, file names) to content-based visual similarity metrics. The system analyzes pixel data, color distributions, and visual features to dynamically determine grouping parameters, allowing the organization structure to adapt to the actual visual content of images rather than relying on predefined folder categories
Solution Approach 2:
The patent creates a universal organization system that can handle diverse image types (photographs, drawings, screenshots, various formats) through a single content-based clustering approach. Instead of requiring different folder structures for different image types, the system universally applies visual similarity analysis across all images, making the organization system adaptable to any image variety without increasing complexity
Data Source
AI summary
The described embodiments relate to method and products for organizing a plurality of images. Specifically, the methods and products can automatically organize a plurality of images into a plurality of groups of images using allocation criteria. The allocation criteria for each image include a similarity distance between that image and at least one other image that measures how similar those images are. Each image can be allocated to at least one similar image group based on the similarity distance. The methods and products can also be used to visualize and display representative images for each of the groups of images.