Sequence constrained hashing algorithm in image retrieval

A technology of image retrieval and hash algorithm, applied in the direction of still image data retrieval, metadata still image retrieval, calculation, etc., to achieve the effect of improving stream distribution, efficient hash coding mechanism, and reducing precision loss

Inactive Publication Date: 2019-01-04
XIAMEN UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a sequence-constrained hash algorithm in image retrieval in order to solve the problems widely existing in previous unsupervised hash learning algorithms

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  • Sequence constrained hashing algorithm in image retrieval
  • Sequence constrained hashing algorithm in image retrieval
  • Sequence constrained hashing algorithm in image retrieval

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Embodiment Construction

[0052] The following examples will further illustrate the present invention.

[0053] The present invention aims to propose a sequence-constrained hashing method, the ultimate goal of which is to obtain the function (hash function) of the hash code:

[0054] H(x)={h 1 (x), h 2 (x),...,h r (x)}.

[0055] The hash function can map the original real feature matrix to the corresponding binary encoding matrix B={b 1 ,b 2 ,...,b n}∈{0,1} r×n , where r is the length of the hash code. The formal description of the proposed detailed scheme is as follows: the formal description of the GIST feature extraction of the query image and the image database to be retrieved is as follows: extract the d-dimensional GIST feature for each image in the training set, and obtain a d×n The original visual feature matrix X={x 1 ,x 2 ,...,x n}∈R d×n , where n represents the number of training samples in the training set, x i The i-th column of the matrix X represents the GIST feature vector ...

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Abstract

A sequence constrained hashing algorithm in image retrieval relates to image retrieval. First of all, in the process of training the model, the relaxation of the original problem usually brings a lotof loss of precision, that is, the model is usually in real space to learn and optimize the model. At the same time, the previous hashing algorithms always keep the point-to-point relationship of theoriginal data in Hamming space, and ignore the nature of the retrieval task, that is, sorting. In order to deal with the problem of large-scale image search and obtain more accurate ranking results bybinary coding, in order to overcome the problems of large-scale image retrieval and improve the use of the model, we can deal with the problem of image search in different feature metric spaces.

Description

technical field [0001] The invention relates to image retrieval, in particular to an efficient sequence-constrained hash algorithm in image retrieval. Background technique [0002] Visual data represented by images and videos is an important source of information for us to understand the objective world. With the rapid development of optical imaging, Internet technology, and high-performance computing, the cost of acquiring, exchanging, and computing visual data such as images and videos has been greatly reduced, leading to an explosive growth in the scale of visual data. For example, according to statistics, as of September 2016, the number of images shared by users on the social networking site Facebook exceeded 450 million per day, and the image data required to be scanned per day has reached 230TB. In the field of public security, urban camera surveillance networks generate massive amounts of surveillance video data all the time. Faced with such a rapid growth rate of ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/58G06K9/62
CPCG06F18/23213G06F18/2411
Inventor 纪荣嵘刘弘
Owner XIAMEN UNIV
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