Image Ranking via Visual Feature Comparison
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Solution Overview
Problem
Image search engines often return a large number of irrelevant or low-quality images due to reliance on text association, making it difficult for users to find relevant and high-quality images.
Innovation Solution
A method that ranks images based on feature comparisons, using predetermined image features such as intensity, color, or edge-based features, to generate a ranking score that identifies the most representative and high-quality images, which are then presented to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image search engines rely on text association to rank images, then the search engine can process images efficiently using existing text search infrastructure, but the relevance and quality of returned images deteriorates
Solution Approach 1:
The patent introduces visual features (color histograms, edge orientations, texture patterns) as an intermediary between the search query and image selection. These visual features serve as a mediator that bridges the gap between text-based search infrastructure and image content, enabling the system to evaluate image relevance based on actual visual characteristics rather than just text associations.
Solution Approach 2:
The patent changes the ranking parameters from text-based metrics to visual feature-based metrics. By computing visual features such as color histograms, edge orientations, and texture patterns, and using these as the basis for image ranking, the system transitions from text-associated ranking to visually-driven ranking, thereby improving image quality while maintaining processing efficiency through automated feature extraction.
2Quantity of substance
If the search engine returns hundreds of results to cover all possible relevant images, then the completeness of search results improves, but the user experience deteriorates due to the large number of results requiring manual review
Solution Approach 1:
The patent applies local quality by evaluating and ranking individual images based on their specific visual features and representativeness to the search query. Instead of treating all results uniformly, the system assigns different qualities and priorities to individual images based on their visual characteristics, allowing users to see only the most relevant images at the top of the results list.
Solution Approach 2:
The patent performs preliminary action by pre-computing visual features for images and pre-ranking them based on their representativeness before presenting results to users. This preliminary processing of visual features and ranking allows the system to prepare and organize results in advance, so that when users search, they immediately see the most relevant images without having to manually review large numbers of results.
3Device complexity
If the search engine uses text near images to determine relevance, then the system can operate with simple keyword matching, but the image quality and representativeness of results deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple visual feature components, including color histograms, edge orientations, and texture patterns. Each of these visual features is analyzed separately and then combined to create a comprehensive representation of the image's content and style, allowing for precise evaluation of image quality and representativeness based on multiple independent visual characteristics.
Data Source
AI summary
Implementations consistent with the principles described herein relate to ranking a set of images based on features of the images determine the most representative and/or highest quality images in the set. In one implementation, an initial set of images is obtained and ranked based on a comparison of each image in the set of images to other images in the set of images. The comparison is performed using at least one predetermined feature of the images.