Visual Content Understanding Subsystem for Automated Image Clustering

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Solution Overview

Problem

Current systems for managing large collections of digital visual content lack efficient methods for automated analysis, classification, and retrieval of visual features, especially in unorganized or unknown datasets, limiting their ability to provide effective search and exploration capabilities.

Innovation Solution

A multi-dimensional visual content realization computing system that employs feature detection algorithms and semantic reasoning techniques to generate semantic labels and compute similarity measures, enabling efficient clustering, searching, and exploration of visual media files without the need for pre-existing metadata, using a visual content understanding subsystem that includes feature detection, indexing, and similarity computation modules.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional manual classification methods are used for visual content, then accuracy of classification may be maintained, but productivity and efficiency deteriorate due to the large scale of digital image collections

Engineering Contradiction:

Improveclassification speedVSAvoidclassification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent replaces manual mechanical classification with automated computer vision systems that use algorithms to detect visual features, generate semantic labels, and classify images. This substitution enables processing of large-scale collections while maintaining consistent accuracy through systematic feature analysis rather than human judgment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables visual content to be automatically classified through self-service mechanisms where the computer vision algorithm independently analyzes visual features, generates semantic labels, and organizes content without human intervention. The automated feature detection and similarity computation perform the classification task autonomously, dramatically improving productivity.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If comprehensive feature analysis is performed on all images, then classification accuracy improves, but use of energy and computational resources worsens

Engineering Contradiction:

Improvefeature detection accuracyVSAvoidcomputational energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by focusing feature detection and analysis only on relevant portions of images rather than processing every pixel uniformly. The computer vision algorithm identifies and analyzes key visual features and regions of interest, achieving accurate classification while reducing overall computational energy consumption compared to exhaustive full-image analysis.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent segments the image processing task into distinct stages: detecting visual features, generating semantic labels, computing similarity measures, and clustering. This segmentation allows the system to apply computational resources efficiently at each stage, performing comprehensive analysis only where necessary rather than uniformly across all images.

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If no pre-existing metadata is available, then adaptability to unknown datasets improves, but the complexity of automated analysis worsens

Engineering Contradiction:

Improvedataset compatibilityVSAvoidanalysis system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces the need for metadata-based organization with a computer vision system that automatically extracts semantic information directly from visual content. The algorithm detects visual features and generates semantic labels without relying on pre-existing metadata, enabling adaptability to unknown datasets while the automated nature of the process manages complexity through systematic feature analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10691743B2Multi-dimensional realization of visual content of an image collection

Publication Date: 2020.06.23 SRI INTERNATIONAL

AI summary

A computing system for realizing visual content of an image collection executes feature detection algorithms and semantic reasoning techniques on the images in the collection to elicit a number of different types of visual features of the images. The computing system indexes the visual features and provides technologies for multi-dimensional content-based clustering, searching, and iterative exploration of the image collection using the visual features and/or the visual feature indices.