Large-scale image library retrieval method based on local similarity hash algorithm

Abstract

The invention provides a large-scale image library retrieval method based on the local similarity hash algorithm. The large-scale image library retrieval method includes the steps that a part of images are selected from an image library to be retrieved to serve as a training image set, and SIFT features of training images are extracted; a K means algorithm is used for conducting clustering on the SIFT features of the training image set to obtain a codebook; the inverse frequency of each code word in the codebook is calculated on the training image set; local sensitive hash coding is conducted on each code word; SIFT features of a queried image and images in the image library to be retrieved are extracted respectively; for each image, the word frequency of each code word in the corresponding image is calculated, and then the weight of each code word is obtained; local similarity hash codes of the images are calculated by using the similarity hash algorithm; the Hamming distances between a hash code of the queried image and the hash codes of the images to be retrieved are calculated; the Hamming distances are used for retrieving the images similar to the queried image rapidly. The large-scale image library retrieval method has good universality, reduces data storage space and also improves the query retrieval efficiency.

Description

technical field [0001] The invention belongs to the technical field of image retrieval, and more specifically relates to a content-based image retrieval method, in particular to a large-scale image library retrieval method based on a local similarity hash algorithm. Background technique [0002] With the rapid development of the Internet, the image data on the Internet is increasing day by day. How to quickly and accurately provide users with the required picture resources is becoming more and more important. Content-based image retrieval (content-based image retrieval, CBIR) technology emerged as the times require, and has attracted the attention of many researchers. Generally speaking, the CBIR system mainly includes two core research contents, one is effective image feature representation, and the other is efficient retrieval algorithm. [0003] In terms of image feature representation, the original CBIR system used the global underlying features of the image, such as c...

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Problems solved by technology

[0007] In view of this, the purpose of the present invention is to propose a large-scale image library retrieval method based on the local similarity hash algorithm to solve the problems of large storage space and slow retrieval speed in image feature databases existing in massive image retrieval

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