Large-scale image library retrieval method based on self-adaptive bit allocation Hash algorithm

Abstract

The invention discloses a large-scale image library retrieval method based on a self-adaptive bit allocation Hash algorithm. The method comprises the following steps: selecting a part of images as a training set from an image library to be retrieved, and extracting a GIST characteristic of the training set; projecting the characteristic data of the training set by using principal component analysis (PCA), and calculating the dispersion of each dimension of training data; according to the dispersion of different dimensions, allocating different bits to encode the data in a self-adaptive manner; obtaining a sub-code according to the code length of each dimension and each dimension of a threshold code, and splicing complete codes of the data in pair; corresponding to the processing and training process of a checked image and the characteristic data in the image library to be retrieved, respectively calculating Hash codes of the image to be retrieved and the characteristics of the checked images; calculating the Hamming distance of the Hash codes, thereby rapidly retrieving similar images. The method is high in universality, the neighbor structure of original characteristic data can be well maintained, and as the data are encoded by using a Hash method, the storage space of data is reduced, and the retrieval efficiency in checking is improved.

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 a large-scale image library retrieval method based on an adaptive bit allocation hash algorithm. Background technique [0002] With the increasing amount of image data on the Internet, how to quickly and accurately provide users with required image resources is becoming more and more important. Content-based image retrieval (content-based image retrieval, CBIR) can solve this problem better, so it has attracted the attention of many researchers. The existing retrieval methods describe the content of the image by extracting the underlying features of the image, and then use feature comparison to judge whether it is a similar image. Therefore, CBIR mainly includes two core research contents, one is effective image feature representation, and the other is efficient retrieval algorithm. The invention mainly solves the p...

AI Technical Summary

Problems solved by technology

[0007] Therefore, the present invention can solve the problem of large storage space and slow retrieval speed in the image feature library for massive image retrieval, and overcomes the shortcomings of LSH, SH, and PCAH methods

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