Visual Content Clustering via Semantic Labeling and Feature Indices
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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, leading to difficulties in searching and clustering unorganized image data.
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 prior tagging or annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual tagging and annotation methods are used for visual content management, then users can organize and retrieve images, but the process becomes extremely time-consuming and impractical for large collections
Solution Approach 1:
The system enables visual content to be automatically analyzed, tagged, and organized without human intervention. Feature detection algorithms automatically extract visual features from images, generate semantic labels, and compute similarity measures, allowing the system to serve itself rather than requiring manual user input for each image
Solution Approach 2:
The patent replaces manual mechanical tagging processes with automated computational systems. Machine learning algorithms and feature detection mechanisms substitute for human users manually examining and labeling images, transforming the mechanical process of visual content management into an automated computational workflow
2Adaptability or versatility
If no automated analysis system is implemented, then the system remains simple, but large collections of visual content cannot be efficiently analyzed, classified, or retrieved
Solution Approach 1:
The system segments the complex task of visual content analysis into distinct modular components: feature detection module, semantic labeling module, similarity computation module, and clustering module. Each module performs a specific function and can be independently developed, tested, and optimized, making the overall complex system manageable and adaptable
Solution Approach 2:
The feature detection algorithms and semantic labeling system are designed to handle multiple types of visual content and various analysis tasks simultaneously. The same core infrastructure supports image retrieval, content-based clustering, similarity search, and exploratory analysis, providing universal functionality across different visual content management scenarios
3Measurement precision
If comprehensive feature extraction is performed on all images, then accurate classification and retrieval are achieved, but computational resources and processing time increase significantly
Solution Approach 1:
The system extracts visual features at multiple levels of detail, from low-level edge and color features to mid-level shape and texture features, and high-level semantic concepts. This partial extraction approach focuses computational resources on the most informative features needed for accurate classification and retrieval, rather than processing all possible image attributes equally
Solution Approach 2:
The system pre-computes and stores visual feature indices for all images in the collection during an offline indexing phase. This preliminary action allows the system to quickly retrieve and compare pre-extracted features during online queries, avoiding the need to re-process entire images during retrieval operations and significantly reducing real-time computational resource consumption
Data Source
AI summary
A computing system and method for identifying related visual content of a collection of visual media files comprising one or more image files and video files includes monitoring inputs to the computing system, the inputs associated with a user interaction with electronic content using the computer system, identifying a visual media file in the collection of visual media files relevant to the electronic content based on a semantic label assigned to the visual media file by the computing system, creating a representative image of the identified visual media file, and displaying the representative image for selection. The computing system enables a selection of the displayed representative image for association of the identified visual media file with the electronic content.