Image Search Ranking via Pre-computed Attribute Sets
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Solution Overview
Problem
Current image search engines face challenges in accurately ranking image results based on visual similarity, often ranking more visually similar images lower than less similar ones, and require extensive data processing to identify relevant images.
Innovation Solution
An image search engine system that continuously analyzes query images and associated images to determine which attributes to heavily weight, using techniques like Kendall's tau distance comparison and weighted distance functions to improve ranking relevance, allowing for efficient identification of relevant image results without storing vast amounts of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image search engines use traditional ranking methods based on basic image attributes, then the system complexity is low, but the ranking accuracy and relevance of image results deteriorate
Solution Approach 1:
The system pre-computes and stores multiple ranked sets of image results based on different image attributes (color, texture, shape, etc.) before a search query is submitted. When a query is received, the system quickly retrieves and combines these pre-computed ranked sets using Kendall's tau distance comparison, avoiding the need for complex real-time computations while maintaining high ranking accuracy.
Solution Approach 2:
The system introduces Kendall's tau distance as an intermediary metric to compare and evaluate different ranked sets of image results. This mathematical tool serves as a mediator to objectively determine which ranking attribute best matches user preferences, enabling accurate ranking selection without requiring complex machine learning models or deep neural networks.
2Reliability
If image search engines store vast amounts of image data for comprehensive search, then the completeness of search results is improved, but the data processing time and resource consumption increase
Solution Approach 1:
The system segments the large image database into multiple subsets, each ranked according to different image attributes (color similarity, texture similarity, shape similarity, etc.). This segmentation allows the system to store and process smaller, organized ranked sets rather than one massive dataset, reducing memory access time and processing overhead while maintaining comprehensive search coverage.
Solution Approach 2:
The system performs preliminary ranking of images by multiple attributes in advance and stores these pre-computed ranked sets. When a search query is submitted, the system only needs to retrieve and combine these pre-existing ranked sets using efficient algorithms, eliminating the need for time-consuming real-time image analysis and comparisons across the entire database.
3Measurement precision
If image search engines perform extensive analysis to determine relevant image attributes, then the relevance of ranking criteria is improved, but the processing overhead and computational resources increase
Solution Approach 1:
The system changes the parameter of ranking from a single composite relevance score to multiple distinct image attributes (color, texture, shape, etc.). By maintaining separate ranked sets for each attribute, the system can quickly switch between different ranking criteria based on the query type without performing extensive re-analysis, thereby improving both relevance and processing efficiency.
Solution Approach 2:
The system computes and stores ranked sets for all possible image attributes in advance (excessive action), but only retrieves and uses the specific attribute(s) relevant to the current query (partial action). This approach ensures that when a query is submitted, the system already has pre-computed rankings for all attributes, eliminating the need for extensive real-time analysis while maintaining the ability to focus on only the necessary attributes for each query.
Data Source
AI summary
A device may be configured to identify a plurality of images that are similar to a query image; generate a plurality of sets of rankings of the identified images based on a plurality of image attributes; compare the generated plurality of sets of rankings of the identified images to a reference set of rankings of images; select, based on the comparing, a particular set of rankings; and rank a plurality of images that are associated with another query image, based on an attribute associated with the selected particular set of rankings.