Information Processing Method for Image Retrieval Efficiency
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Solution Overview
Problem
Current image retrieval systems face challenges in efficiently and accurately processing massive image data due to high data dimensions, limited computing capabilities, and poor universality across different feature extraction algorithms, leading to low query efficiency and convenience.
Innovation Solution
The proposed method involves obtaining initial feature information, performing feature processing to achieve target feature information with a preset dimension through dimensionality reduction, equalization, or expansion, and subsequent similarity comparisons to retrieve relevant images, utilizing a terminal device with a feature extraction unit, processing unit, and secondary retrieval unit for efficient image retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If feature dimension processing is performed to reduce data dimension to preset dimension, then retrieval efficiency is improved, but feature information completeness may be lost
Solution Approach 1:
The feature processing is divided into two distinct stages: dimensionality reduction processing and feature equalization processing. The dimensionality reduction stage compresses high-dimensional feature data to preset dimensions to improve retrieval efficiency, while the subsequent feature equalization stage recovers and balances the feature information distribution to minimize information loss, thus resolving the contradiction between efficiency and completeness
Solution Approach 2:
Feature equalization processing is performed as a preliminary or concurrent operation alongside dimensionality reduction. By pre-processing features to ensure uniform distribution and applying equalization during the reduction process, the system maintains feature information quality while achieving efficient dimensionality reduction for fast retrieval
2Measurement precision
If high data dimension feature information is processed, then retrieval accuracy is improved, but computing capability requirements increase
Solution Approach 1:
The system dynamically adjusts feature dimension parameters based on computing resource availability. By transforming feature data between different dimensional representations (high-dimensional for accuracy, preset-dimension for efficiency) and applying feature equalization to maintain quality, the system adapts computing requirements to match available power while preserving retrieval accuracy
3Adaptability or versatility
If feature dimension processing is applied, then universality across different algorithms is improved, but processing complexity increases
Solution Approach 1:
The feature equalization processing module serves multiple functions simultaneously: it equalizes feature distribution for improved retrieval, enables compatibility across different feature extraction algorithms through standardized processing, and works with various dimensionality reduction techniques. This multi-functional approach achieves universality while managing complexity through a unified processing framework
Data Source
AI summary
An information processing method is disclosed. The method includes: obtaining initial feature information of to-be-queried information; performing feature processing on the initial feature information of the to-be-queried information, to obtain target feature information of the to-be-queried information, where the feature processing includes at least feature dimension processing used to process a data dimension of the initial feature information of the to-be-queried information to be a preset dimension; performing a first retrieval operation on m pieces of candidate information based on the target feature information of the to-be-queried information, to obtain a first retrieval result; and performing a second retrieval operation on the first retrieval result based on the initial feature information of the to-be-queried information, to obtain result information corresponding to the to-be-queried information. The method can quickly and efficiently implement information retrieval.