Image retrieval method based on deep learning and Hash

An image retrieval and deep learning technology, which is applied in the field of image retrieval based on deep learning and hashing, can solve the problems of quantization error loss and low retrieval accuracy, and achieve the effects of reducing quantization error, improving accuracy, and enhancing expression ability

Active Publication Date: 2016-04-20
ZHENGZHOU JINHUI COMP SYST ENG
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Problems solved by technology

[0004] Aiming at the deficiencies in the prior art, the present invention provides an image retrieval method based on deep learning and hashing, and simultaneously learns image features and a deep convolutional network architecture for hash coding, adding a hash layer and continuous Quantization error loss caused by qu...

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  • Image retrieval method based on deep learning and Hash
  • Image retrieval method based on deep learning and Hash
  • Image retrieval method based on deep learning and Hash

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Embodiment 1

[0023] Embodiment one, see figure 1 As shown, an image retrieval method based on deep learning and hashing, specifically includes the following steps:

[0024] Step 1. Divide the image data set and its corresponding category label information into two parts, one part is used as a training sample set, and the other part is used as a test sample set, wherein each sample in the training sample set and the test sample set includes an image and the corresponding category labels;

[0025] Step 2. Build a deep convolutional neural network architecture. The deep convolutional neural network architecture includes a convolutional subnetwork, a hash layer, and a loss layer. The convolutional subnetwork is used to learn image features, and the hashing layer includes a fully connected layer and an activation layer. And the thresholding layer, used to learn the construction of the hash function and get the hash code of the input image, the loss layer includes a Softmax classifier loss modu...

Embodiment 2

[0030] Embodiment two, see figure 1 As shown, an image retrieval method based on deep learning and hashing, specifically includes the following steps:

[0031]Step 1. Divide the image data set and its corresponding category label information into two parts, one part is used as a training sample set, and the other part is used as a test sample set, wherein each sample in the training sample set and the test sample set includes an image and the corresponding category labels;

[0032] Step 2. Build a deep convolutional neural network architecture. The deep convolutional neural network architecture includes a convolutional subnetwork, a hash layer, and a loss layer. The convolutional subnetwork includes multiple convolutional layers, pooling layers, and fully connected layers. Use For learning image features, the hash layer includes a fully connected layer, an activation layer, and a thresholding layer, which are used to learn the construction of a hash function and obtain the ha...

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Abstract

The invention relates to an image retrieval method based on deep learning and Hash. According to the image retrieval method, on the basis of powerful learning capacity of a deep convolutional neural network, deep features of images are extracted, and the problems of weak feature expression capacity and low retrieval precision caused by use of lower features of the images in the prior art are solved; a Hash layer is introduced for construction of a Hash function, learning of the deep features of the images and the construction of the Hash function are completed in the same process, an internal relation of the image features and the Hash function is explored, and the accuracy rate of the image retrieval is greatly increased; quantization error loss is added to a loss layer of the deep convolutional neural network, the expression capacity of Hash codes is enhanced, by means of a Softmax classifier loss module and a quantization error loss module, quantization errors caused by binaryzation in the Hash function are effectively reduced, and the accuracy rate of the image retrieval is further increased.

Description

technical field [0001] The invention relates to the field of image retrieval, in particular to an image retrieval method based on deep learning and hashing. Background technique [0002] With the advent of the era of big data, Internet image resources are growing rapidly, and how to quickly and effectively retrieve large-scale image resources to meet user needs needs to be solved urgently. In order to perform fast and efficient retrieval in large-scale image collections, hashing technology maps original images to binary hash codes while maintaining similarity. Due to the efficiency of binary hash codes in Hamming distance calculation and the advantages of storage space, hash codes are very efficient in large-scale image retrieval. Content-based image retrieval realizes the content expression of images by extracting the underlying visual features of images. Compared with these underlying features, the deep convolutional neural network is more able to obtain the intrinsic fe...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/583
Inventor 张晨民赵慧琴彭天强
Owner ZHENGZHOU JINHUI COMP SYST ENG
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