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Image Hash coding method based on deep learning

A technology of hash coding and deep learning, applied in the field of coding, can solve the problem of inconsistency of binary code feature representation, and achieve the effect of overcoming the inconsistency between hash coding and image features

Active Publication Date: 2017-08-04
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

Manually designed features are more inclined to describe the visual information of the image rather than its semantic information. In addition, the two stages of feature learning and hash function learning will cause the generated binary code to be inconsistent with the feature representation.

Method used

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  • Image Hash coding method based on deep learning
  • Image Hash coding method based on deep learning
  • Image Hash coding method based on deep learning

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Embodiment

[0074] The following takes the CIFAR image dataset as an example for further explanation. The image dataset contains 60,000 images with 10 categories of labels, including airplanes, boats, cars, animals, etc. First, randomly 50,000 images in the data set are used as the training set, and 10,000 images are used as the test set.

[0075] The pre-trained image classification model GoogLeNet on the ImageNet image recognition dataset is used to complete the hash coding task of the image. Replace the classification layer with 1000 units in the last layer of GoogLeNet with a hash layer. The number of units in the hash layer is the number of bits that the image is encoded into a binary code. For example, 48 bits define 48 units.

[0076] Then, optimize the parameters of the GoogLeNet model set above. The process carries out 50,000 iterations, and each iteration randomly selects 50 images and their corresponding labels from the training set images and inputs them into GoogLeNet. has...

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Abstract

The invention discloses an image Hash coding method based on deep learning. The method includes following steps: step 1, adopting an image classification model GoogLeNet trained at an ImageNet image recognition database as an initialized basic network structure, and replacing the final classification layer of the GoogLeNet model by a Hash layer, wherein the unit number of the Hash layer is the bit number to be coded by images; step 2, optimizing parameters of the GoogLeNet model; and step 3, inputting images in an image retrieval data set to the optimized GoogLeNet model, and quantifying the floating point number output by the GoogLeNet model to binary codes to obtain the binary code of each image. According to method, combined optimization of image characteristics and a Hash function is realized, and the defect that the Hash codes obtained by learning of the conventional Hash method is not in accordance with the image characteristics is overcome.

Description

technical field [0001] The present invention relates to an encoding method, in particular to an image hash encoding method based on deep learning. Background technique [0002] With the rapid growth of the number of images on the network, content-based image retrieval is becoming more and more important, and hashing technology has received more and more attention. The goal of hashing technology is to construct a hash function that maps data from the original space to compressed binary code while preserving the data structure of the original space. Hashing is a powerful technique for nearest-neighbor lookups due to the efficiency of computation and storage due to the compressed binary code. The flow of most hash coding methods is: first extract the hand-designed feature representation of the image, and then learn the hash function on this basis. Manually designed features are more inclined to describe the visual information of the image rather than its semantic information....

Claims

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Application Information

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IPC IPC(8): G06T9/00
CPCG06T9/001G06T9/002
Inventor 颜成钢杨东宝孙垚棋彭冬亮张勇东薛安克
Owner HANGZHOU DIANZI UNIV
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