Large-Scale Image Database Retrieval Method Based on Optimal K-means Hashing Algorithm

A hash algorithm and image library technology, applied in still image data retrieval, still image data in vector format, calculation, etc., can solve the problems of slow retrieval speed and large storage space of image feature library

Active Publication Date: 2017-09-12
INST OF AUTOMATION CHINESE ACAD OF SCI
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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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  • Large-Scale Image Database Retrieval Method Based on Optimal K-means Hashing Algorithm
  • Large-Scale Image Database Retrieval Method Based on Optimal K-means Hashing Algorithm
  • Large-Scale Image Database Retrieval Method Based on Optimal K-means Hashing Algorithm

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[0079] In order to make the purpose, technical solutions and advantages of the present invention clearer, the specific implementation manners of the present invention will be described in detail below in combination with the technical solutions and accompanying drawings.

[0080] Take the FLICKR1M (see the article Mark J. Huiskes, Michael S. Lew, "The MIR Flickrretrieval evaluation", In Proceedings of ACM International Conference on Multimedia Information Retrieval, 2008) dataset as an example for illustration. FLICKR1M contains 1 million images, all downloaded from the Flickr website, of varying content and size.

[0081] A kind of large-scale image library retrieval method based on optimal K-means hash algorithm proposed by the present invention comprises the following steps:

[0082] For the images in the image library, select a part of the images as the training image set;

[0083] For image database and training set, extract GIST global features as retrieval features;

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Abstract

A large-scale image library retrieval method based on the optimal K-means hash algorithm, comprising: selecting some images from the image library to be retrieved as a training image set, first extracting the GIST features of the images in the training set; Preprocessing of feature value assignment; dividing the preprocessed feature data into multiple subspaces; training the codebook and codebook encoding of the subspace for each subspace; processing the feature data in the image library to be retrieved and the query image Corresponding to the training process, extract the GIST features of the retrieval image and the query image respectively, then calculate the hash codes of the features of the image to be retrieved and the query image, and then calculate the Hamming distance between the feature codes of the image to be retrieved and the feature code of the query image , so as to quickly retrieve similar images. The invention has good universality, not only reduces the storage space of data but also improves the retrieval efficiency of query.

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 (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. Describe the image content by extracting the features of the image, such as GIST features (for details, please refer to the article Aude Oliva an...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/56G06F16/5838G06F18/2411G06F18/2413G06F18/24147
Inventor 张树武张桂煊郭勤振曾智
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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