Image retrieval method based on deep hash and quantification and storage medium

An image retrieval, depth technology, applied in still image data retrieval, still image data indexing, character and pattern recognition, etc., can solve the problem of unsolved quantization error hash function independence problem

Active Publication Date: 2020-01-14
CHONGQING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

[0006]A key shortcoming of the deep hashing methods mentioned above is that the quantization error from co

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  • Image retrieval method based on deep hash and quantification and storage medium
  • Image retrieval method based on deep hash and quantification and storage medium
  • Image retrieval method based on deep hash and quantification and storage medium

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Embodiment Construction

[0063] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0064] The technical scheme that the present invention solves the problems of the technologies described above is:

[0065] Such as figure 1 Shown, the realization process of the present invention comprises as follows:

[0066] Step 1: Randomly generate two images from the image database as the input of the network, one is the training image I 1 , and the other one is the query image I 2 , for image preprocessing;

[0067] Step 2: Construct a convolutional neural network, using the Alexnet model structure as the basic architecture. The convolutional neural network contains 5 convolutional layers, 3 pooling layers, 2 fully connected layers, and 1 hashing layer;

[0068] The specific implementation of ...

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Abstract

The invention discloses an image retrieval method based on deep hash and quantification and a storage medium. Firstly, a training set and a test set are established, images needing to be recognized are preprocessed, then a convolutional neural network is constructed, an Alexnet model structure is adopted as a basic framework, data pairs are generated at any time through training samples, trainingis conducted according to the convolutional neural network, and a corresponding output value Zn is obtained. By processing an image category through a Glove model to obtain an embedded label V, calculating an error function of an output value of the convolutional neural network by combining the embedded label V, updating network parameters, processing a query image and a database image by using atrained model to obtain corresponding binary codes, calculating inner product similarity by using an asymmetric distance quantification method, a retrieval result is output. According to the method, the block coding module is introduced, and the elaborately designed hybrid network and the specified loss function are utilized to jointly learn the deep-view semantic tags, so that the accuracy of image retrieval is greatly improved.

Description

technical field [0001] The invention belongs to the technical fields of artificial intelligence and image retrieval, and in particular relates to an image retrieval method based on deep hashing and quantization. Background technique [0002] Images are an important source of people's cognition of themselves and the world. With the rapid development of information science and technology, people's demand for information is also increasing. It is becoming more and more convenient to obtain images from the Internet. At the same time, the current Social networking is also becoming more popular. In the face of massive data, how to organize and effectively use these data has become a problem to be solved. In addition, image retrieval technology has a wide range of applications in security, insurance, entertainment, and social and people's livelihood. [0003] Image retrieval is to find similar images based on the target image. In the face of a large number of image processing, th...

Claims

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Application Information

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IPC IPC(8): G06F16/51G06K9/62G06N3/04
CPCG06F16/51G06N3/045G06F18/214
Inventor 甘玲张天振熊子文
Owner CHONGQING UNIV OF POSTS & TELECOMM
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