Image Analysis System for User Connection Discovery
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Solution Overview
Problem
Access to social graphs, which are crucial for discovering user connections and communities in social media, is often limited due to privacy concerns and restricted data accessibility, making it difficult for researchers and service providers to effectively utilize user connections and communities for personalized services and recommendations.
Innovation Solution
An image analysis system that utilizes machine-generated tags based on visual features of user-shared images to discover connections and communities, allowing for the generation of user profiles and recommendations without relying on explicit social graph data, using techniques like Bag-of-Feature Tagging (BoFT) for image annotation and community discovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If social graph data is used to discover user connections, then connection discovery accuracy is improved, but data accessibility deteriorates due to privacy concerns and restricted access
Solution Approach 1:
The patent uses user-shared images as an intermediary data source to infer user connections and characteristics. Instead of directly accessing restricted social graph data, the system analyzes publicly available images that users share, extracting visual features and tags that indirectly reveal user attributes, connections, and community affiliations without requiring access to private social graph structures
Solution Approach 2:
The patent replaces the mechanical approach of directly querying social graph databases with an automated image analysis system using computer vision and machine learning. The system automatically extracts visual features, generates tags, and infers user characteristics from images, substituting manual or direct social graph access with automated visual content analysis
2Ease of manufacture
If user-generated tags are used for image annotation, then implementation simplicity is improved, but annotation accuracy deteriorates
Solution Approach 1:
The patent replaces manual user-generated tagging with automated machine-generated tags based on computer vision analysis. The system uses visual feature extraction and image processing algorithms to automatically generate accurate annotations, substituting the simple but inaccurate user-tagging mechanism with a more complex but precise automated analysis system
Solution Approach 2:
The system enables images to annotate themselves through automated visual analysis rather than relying on external user input. The image content directly generates its own tags and labels through machine learning models that analyze visual features, allowing the data to self-describe without requiring human intervention or subjective user interpretation
Data Source
AI summary
A system for annotating user images with non-user generated labels corresponding to a plurality of users and generating user profiles includes: one or more processor-readable media, configured to store processor-executable instructions for image analysis and user profile generation; and one or more processors, configured to, based on execution of, the stored processor-executable instructions: annotate user images with non-user-generated labels, wherein the non-user-generated labels correspond to visual features of the user images and generate user profiles based on non-user-generated tags associated with images corresponding to each respective user.