Image retrieval method based on minimum projection errors of multiple hash tables

Abstract

An image retrieval method based on minimum projection errors of multiple hash tables belongs to the technical field of image retrieval, and is characterized in that the gist features of an image to be retrieved, a training image and a query image are respectively extracted; the principal component direction of training features is calculated and optimized through the iterative quantization method, and features to be retrieved and query features are projected on the optimized principal component direction to acquire the corresponding hash codes; the training features go through energy reduction to get new training features, and the process is repeated until the Num groups of hash codes are acquired; and the Hamming distance between the Num group of hash codes of the query image and the Num group of hash codes of the image to be retrieved is calculated, so that the similarity between the image to be retrieved and the query image can be measured according to the distance. The invention has the effects and benefits that the image retrieval method overcomes the shortcoming that the Hamming spherical radius of a single harsh table is large in case of a high recalling rate, as well as the problem that random projection hashing needs too many hash tables in case of a high recalling rate.

Description

technical field [0001] The invention belongs to the technical field of image retrieval and relates to a content-based image retrieval method, in particular to an image retrieval method based on the minimization of multi-hash table mapping errors. Background technique [0002] Given a query image, the task of image retrieval is to find images similar to it from an image library. The traditional image retrieval method is to represent the image as a high-dimensional Euclidean vector, and use the method of linear scanning image library to search. For a massive image library, the required feature storage space is very large, and the linear search of the image library is very time-consuming. The image hash method greatly reduces the storage space of the feature by encoding the high-dimensional Euclidean feature into a concise binary hash code; at the same time, the approximate nearest neighbor method is used to search for similar images, which effectively improves the search effi...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to solve the problem of large storage space and slow retrieval speed of the image feature library when retrieving massive images, and to overcome the shortcomings of the existing hash method with low accuracy when the recall rate is large, and propose a method based on Doha Image Retrieval Method with Minimization of Hierarchical Mapping Error

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