Spatial Image Organization via Emotion Face Cloud Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in efficiently organizing and visualizing large collections of images, particularly due to information bloat from redundant photos and the difficulty in navigating and finding specific images within these collections, exacerbated by overlapping images and limited screen real estate.
Innovation Solution
A computer-implemented method for spatially organizing images by obtaining attribute vectors, sorting and clustering them based on similarity, and displaying representative images within a defined topology, such as a line, circle, or sphere, to facilitate navigation and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all images in the collection are displayed, then complete information is presented, but screen real estate is exceeded and navigation becomes difficult
Solution Approach 1:
The patent divides the large collection of images into multiple clusters based on attribute similarity. Each cluster represents a group of images with comparable characteristics, allowing the system to display a manageable number of cluster representatives on screen while still representing the entire large collection. This segmentation resolves the contradiction by organizing images into discrete groups that can be navigated without overwhelming the display area.
Solution Approach 2:
The patent introduces a topological space dimension for organizing and navigating image clusters. Instead of displaying all images in a flat 2D grid that overwhelms screen real estate, the system maps clusters into a topological space where similar images are positioned closer together. This dimensional transformation allows efficient navigation through large collections while maintaining screen real estate constraints.
2Productivity
If similar images are grouped together, then navigation efficiency improves, but computational complexity increases
Solution Approach 1:
The patent changes the parameter representation of images from raw pixel data to extracted attribute vectors (e.g., color histograms, texture features, semantic tags). This parameter transformation enables efficient similarity computation and clustering by comparing meaningful attributes rather than entire image contents, thus improving navigation efficiency while controlling computational complexity through dimensionality reduction.
Solution Approach 2:
The patent performs preliminary clustering and organization of images into topological spaces before user interaction. By pre-computing attribute vectors, similarity metrics, and cluster assignments, the system prepares the navigation structure in advance, reducing the computational burden during actual user navigation and improving overall productivity without excessive real-time complexity.
3Loss of information
If attribute-based clustering is performed, then image organization improves, but processing time increases
Solution Approach 1:
The patent applies different attribute extraction and clustering strategies to different regions or types of images within the collection. Rather than uniformly processing all images with computationally intensive attribute analysis, the system identifies local characteristics and applies appropriate organization methods selectively, improving overall organization quality while reducing total processing time through targeted computation.
Data Source
AI summary
A collection of photos is organized by arranging a limited number of clusters of the photos on a predefined topology, so that similar photos are placed in the same cluster or a nearby cluster. Similarity is measured in attribute space. The attributes may include automatically recognized facial expression attributes.