A Large-Scale Image Database Retrieval Method Based on Adaptive Bit Allocation Hashing Algorithm

Abstract

A large-scale image database retrieval method based on the adaptive bit allocation hash algorithm, comprising: selecting some images from the image database to be retrieved as a training set, extracting the GIST features of the training set; using PCA to project the feature data of the training set , and then calculate the dispersion of each dimension for the training data; according to the dispersion of different dimensions, adaptively allocate different bits to encode data; encode each dimension according to the encoding length and threshold of each dimension, and obtain sub-coding, Splicing the complete encoding of the paired data; the processing of the query image and the feature data in the image database to be retrieved corresponds to the training process, and the hash codes of the features of the image to be retrieved and the query image are calculated respectively; the Hamming distance between the two is calculated, This enables quick retrieval of similar images. The invention has good universality, can well keep the neighbor structure of the original feature data, and uses the hash method to code the data, which not only reduces the storage space of the data but also improves the retrieval efficiency of the query.

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...

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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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