Image Search System Using Cascade Feature Extraction
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Solution Overview
Problem
Current image search engines face challenges in accurately retrieving similar images from external databases without pre-defined descriptive information, leading to poor search results and user inconvenience, as they rely heavily on user-inputted category and style information which may be inaccurate.
Innovation Solution
The system automatically determines category and descriptive information for query images by comparing visual features with those in the database, using a cascade-type re-search method that extracts and compares global and local features, eliminating the need for user-provided metadata and improving search accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system requests users to provide category, style information, and other descriptive information when inputting query images, then search accuracy is improved, but the search process becomes cumbersome and user experience deteriorates
Solution Approach 1:
The system performs self-service by automatically extracting category information, style information, and descriptive information from the query image itself using image recognition technology. This eliminates the need for users to manually input these details, thereby maintaining high search accuracy while significantly improving search process convenience and user experience.
2Productivity
If users manually input descriptive information for query images, then search results may be obtained, but the inputted information may be inaccurate leading to incorrect search results
Solution Approach 1:
The patent replaces the manual mechanical input system with an automated image recognition system. Instead of relying on users to manually input descriptive information (which may be inaccurate), the system uses computer vision algorithms to automatically extract and analyze visual features from the query image, thereby improving both search efficiency and information accuracy simultaneously.
3Measurement precision
If the system conducts comprehensive visual feature extraction and comparison, then search accuracy is improved, but system resource consumption increases
Solution Approach 1:
The patent applies segmentation by dividing the visual feature extraction and comparison process into distinct modules: global feature extraction, local feature extraction, and hierarchical comparison. This modular approach allows the system to process images efficiently by extracting only relevant features at appropriate levels of detail, thereby maintaining high search accuracy while reducing overall system resource consumption compared to exhaustive full-image processing.
Data Source
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AI summary
Embodiments of the present application relate to a method for searching images, a system for searching images, and a computer program product for searching images. A method for searching images is provided. The method includes receiving an input query image, extracting visual features from the inputted query image; determining a similarity of the visual features of the query image and visual features of images in an image database; determining category information, descriptive information, or a combination thereof associated with the query image based on category information, descriptive information, or a combination thereof of business objects corresponding to images having a similarity to the query image that complies with a precondition; conducting searches of the images based on the query image and the category information, the descriptive information, or a combination thereof associated with the query image; and returning search results.