Landmark Image Search Ranking via Visual Clustering
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Solution Overview
Problem
Existing image search systems face challenges in retrieving representative and diverse views of landmarks from community-contributed collections on the web, due to inaccurate text annotations and varying image quality, making it difficult to browse and visualize relevant content.
Innovation Solution
A system that ranks images within visual clusters based on low-level self-similarity scores, discriminative modeling scores, and point-wise linking scores, generating a final ranked list of representative images without requiring human-prepared examples or gazetteer tagging, using unsupervised learning and content- and context-based tools to compile landmark image search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If community-contributed image collections are used to provide landmark images, then the volume and availability of images increase, but the accuracy of text annotations and image quality deteriorate
Solution Approach 1:
The system performs self-service by automatically generating visual clusters and selecting representative images without requiring manual annotation verification. The unsupervised learning algorithm independently processes the community-contributed images, extracts visual features, and identifies representative images based on visual similarity, eliminating the need for human intervention to ensure annotation accuracy.
Solution Approach 2:
The patent replaces manual annotation verification and human review processes with automated computer vision algorithms. The system uses unsupervised learning and visual feature extraction to substitute human judgment, automatically clustering images and selecting representatives based on visual content rather than relying on potentially inaccurate user-provided text annotations.
2Quantity of substance
If the volume of landmark images in a collection increases, then the comprehensiveness of the collection improves, but the difficulty of browsing and representing image content increases
Solution Approach 1:
The system segments the large collection of landmark images into smaller visual clusters based on visual similarity. Each cluster groups images with comparable visual characteristics, making the overwhelming volume of images manageable and browsable. Users can navigate through organized clusters rather than facing an unstructured sea of images, significantly improving ease of operation.
Solution Approach 2:
The patent introduces a new organizational dimension by clustering images based on visual features rather than traditional metadata or file structures. This visual similarity dimension transforms the flat, unstructured image collection into a hierarchical, multi-dimensional space where images are naturally grouped, enabling more effective browsing and representation.
3Extent of automation
If unsupervised learning is used to rank images without human-prepared examples, then the need for human intervention is reduced, but the complexity of the system increases
Solution Approach 1:
The system replaces complex human-curated annotation processes with automated unsupervised learning algorithms. Although the computational complexity increases, the operational complexity decreases as the system autonomously performs image clustering and representative selection without human intervention, achieving high automation in the image ranking process.
Data Source
AI summary
This patent discloses a system to compile a landmark image search result. The system may determine a rank of each image within a visual cluster according to at least one of a low-level self-similarity score, a low-level discriminative modeling score, and a point wise linking score. The landmark image search result may be compiled as a function of the rank of each image.