Image Retrieval Hash Segmentation
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Solution Overview
Problem
The existing image retrieval technologies face inefficiencies due to the complexity and time-consuming nature of calculating similarity between images, especially when dealing with large databases, leading to low retrieval efficiency.
Innovation Solution
The method involves obtaining a hash value of a reference image, splitting it into segments, and using these segments to retrieve matching hash value segments from target hash tables, allowing for the identification of candidate images with similar features by calculating hamming distances only between these candidates and the reference image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If similarity calculation is performed between the reference image and each image in the database, then comprehensive image retrieval is achieved, but the data processing amount becomes excessively large and retrieval efficiency becomes excessively low
Solution Approach 1:
The patent divides the hash value of each image into multiple segments (e.g., 4 segments of 16 bits each from a 64-bit hash value). Instead of comparing complete hash values, the system creates separate hash tables for each segment position and compares only corresponding segments. This segmentation reduces the comparison scope from N images to a subset of images sharing at least one matching segment, significantly improving retrieval efficiency while maintaining comprehensive search capability.
2Measurement precision
If similarity calculation is performed between the reference image and each image in the database, then all candidate images are evaluated, but the calculation complexity and time consumption become excessively high
Solution Approach 1:
The patent pre-divides hash values into segments and organizes them into multiple hash tables before retrieval operations. During retrieval, the system first performs quick segment matching to identify candidate images, then calculates full similarity only for these candidates. This preliminary segmentation and indexing action reduces the complexity of subsequent similarity calculations by filtering out non-matching images beforehand.
3Quantity of substance
If the database contains a large quantity of images, then comprehensive coverage is achieved, but the calculation amount for similarity comparison becomes excessively large
Solution Approach 1:
The patent transforms the one-dimensional problem of comparing complete hash values into a multi-dimensional approach by creating multiple hash tables, each corresponding to a specific segment position. This dimensional transformation allows the system to query along different segment dimensions and intersect results, efficiently handling large databases by reducing the search space from O(N) to O(N/k) where k is the number of segments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces the data processing amount and improves image retrieval efficiency by focusing on a smaller set of candidate images with matching hash value segments, thereby speeding up the retrieval process.
Implementation Method 1
calculating a hamming distance between each of the candidate images and the reference image according to the first hash value of the reference image and the second hash values of the candidate images; and retrieving, from the candidate images to which the second hash value segments respectively belong, the target image whose hamming distance to the reference image is less than a preset threshold
Data Source
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AI summary
A method, device, system, and server for image retrieval, and a storage medium. The method comprises: acquiring first hash value of a reference image to be retrieved; splitting the first hash value into a preset number of first hash value segments, where the different first hash value segments are different in terms of positional order in the first hash value; positioning a target hash table on the basis of the positional order of the first hash value segments, retrieving from multiple hash value segments in the target hash table second hash value segments identical to the first hash value segments, the target hash table being used for storing hash value segments found on the positional order of the first hash value segments in hash values of images; and retrieving a target image similar to the reference image from candidate images to which the second hash value segments belong. The method reduces the data processing volume in an image retrieval process and increases the efficiency of image retrieval.