Image Search Accuracy via Multi-Granularity Mining Network
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Solution Overview
Problem
Existing image search technologies face challenges in accurately and efficiently searching for images with similar objects due to the inability to distinguish between background and object features, leading to inaccurate feature extraction and reduced search results.
Innovation Solution
The implementation of a multi-granularity mining network (MGMN) with an adaptive spatial attention unit (ASAU) that employs deep learning to highlight target areas, filter out background information, and combine features from different scales, enhancing the accuracy of image search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based AI systems are used to improve recognition rates and understanding of user preferences, then the accuracy of image feature extraction is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the image processing task into distinct functional modules: a backbone network for extracting basic feature information, and a feature representation network for processing and refining these features. This segmentation allows each module to specialize in specific operations, improving overall accuracy while enabling independent optimization and parallel processing to mitigate computational complexity.
Solution Approach 2:
The backbone network performs preliminary feature extraction on the input image before the feature representation network processes it further. By pre-processing the image to extract basic features first, the system reduces the computational burden on subsequent stages while maintaining high accuracy in the final feature representation.
2Loss of information
If feature extraction includes both background and object information, then comprehensive image representation is achieved, but the precision of target object identification decreases
Solution Approach 1:
The feature representation network extracts and isolates the target object features from the comprehensive feature information generated by the backbone network. By separating the target object information from background and other irrelevant information, the system achieves precise target identification while retaining the option to use comprehensive features when needed.
Solution Approach 2:
The patent applies different processing qualities to different parts of the feature information. The feature representation network focuses computational resources on enhancing the quality of target object features while applying lighter processing to background information, thereby improving target identification accuracy without completely discarding comprehensive image representation.
Data Source
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AI summary
A method and apparatus for image searching based on artificial intelligent (AI) are provided. The method includes obtaining first feature information by extracting features from an image based on a first neural network, obtaining second feature information corresponding to a target area of a query image by processing the first feature information based on a second neural network and at least two filters having different sizes, and identifying an image corresponding to the query image according to the second feature information.