Image Search Query Grouping by Importance Levels
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Solution Overview
Problem
Existing similar image search techniques are inefficient for creating dictionaries of various images and require user evaluation to determine importance levels, leading to potential noise in search results and decreased work efficiency.
Innovation Solution
An image search system that accumulates image feature values, allows input of multiple query images, extracts and searches these values, displays similar images, and determines the importance level of query images based on search results, grouping and rearranging them for enhanced precision and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple query images are used for similar image search to collect various images efficiently, then productivity is improved, but reliability deteriorates due to noise inclusion in search results
Solution Approach 1:
The patent segments the query images into multiple query groups based on their feature values and similarity relationships. By dividing the single large query set into smaller homogeneous groups, the system can process each group separately, reducing noise impact while maintaining high productivity through batch processing.
Solution Approach 2:
The patent changes the parameter of query image selection by automatically determining importance levels and selecting only high-importance images as query images. This parameter change filters out low-quality or noisy images before they can contaminate the search results, thereby improving reliability while maintaining efficiency.
2Reliability
If user evaluation is required to determine importance level of query images, then reliability is improved, but productivity deteriorates due to increased manual work
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine the importance level of query images through feature value analysis and similarity computation. The system serves itself by autonomously identifying high-importance images without requiring user intervention, thereby maintaining reliability while eliminating manual work and improving productivity.
Solution Approach 2:
The patent substitutes the mechanical system of manual user evaluation with an automated computational system that uses feature value comparison and similarity algorithms. This replacement eliminates human labor while maintaining or improving the accuracy of importance determination through objective mathematical measures.
3Manufacturing precision
If manual dictionary creation is performed to ensure recognition accuracy, then manufacturing precision is improved, but productivity deteriorates due to increased manpower costs
Solution Approach 1:
The patent applies preliminary action by automatically performing feature value extraction, similarity computation, and importance level determination before the dictionary creation process. This preliminary automated processing prepares high-quality candidate images and features in advance, ensuring dictionary precision while reducing the manual effort required during the actual dictionary assembly stage.
Solution Approach 2:
The patent substitutes manual dictionary creation operations with automated computational processes that extract features, compute similarities, and select high-importance images. This mechanical substitution maintains or improves dictionary quality through consistent algorithmic processing while dramatically increasing creation efficiency by eliminating manual labor.
Data Source
AI summary
An image search device stores images and tag information about the images, receives an image, extracts, from the image, feature values for search queries a similar image search unit which performs a similar-image search to obtain similar images and grouping information, divides the queries into groups in accordance with the obtained grouping information, calculates the levels of importance of the groups, sorts the search results in accordance with the levels of importance of the groups, and outputs information about the search results for each group.