Massive image library retrieving method based on optimal K mean value Hash algorithm

Abstract

A massive image library retrieving method based on an optimal K mean value Hash algorithm comprises the steps that part of images are selected from an image library to be retrieved to serve as a training image set, and firstly GIST characteristics of images of the training image set are extracted; characteristic value allocation preprocessing is conducted on characteristic data of the training image set; the preprocessed characteristic data are divided into a plurality of sub-spaces; a codebook and codes of the codebook of the corresponding sub-space are trained out for each sub-space; the processing and training process of characteristic data in the image library to be retrieved corresponds to the processing and training process of characteristic data in inquiring images, the GIST characteristics of images to be retrieved and the GIST characteristics of the inquiring images are extracted respectively, then Hash codes of the characteristics of the images to be retrieved and Hash codes of the characteristics of the inquiring images are calculated, the Hamming distance between the codes of the characteristics of the images to be retrieved and the codes of the characteristics of the inquiring images is calculated, and thus similar images are fast retrieved. The massive image library retrieving method based on the optimal K mean value Hash algorithm has good universality, the storage space for data is reduced, and the inquiring retrieving efficiency is improved.

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 database retrieval method based on an optimal K-means hash algorithm. Background technique [0002] With the rapid development of the Internet, image data on the Internet is increasing day by day, how to quickly and accurately provide users with required image resources is becoming more and more important. Content-Based Image Retrieval (CBIR) technology emerges at the historic moment 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. Describe the image content by extracting the features of the image, such as GIST features (for details, please refer to the article Aude Oliva and Antonio Torralba, "Modeling ...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the defects of the above-mentioned KH, the present invention proposes a large-scale image library retrieval method based on the optimal K-means hash algorithm to solve the problems of large storage space and slow retrieval speed in image feature databases that exist when searching for massive images

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