Image Retrieval Based on Deep Convolutional NeuralNetworks and Binary Hashing Learning

PENG Tianqiang; LI Fang

doi:10.11999/JEIT151346

Volume 38 Issue 8

Sep. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(8): 2068-2075

PENG Tianqiang, LI Fang. Image Retrieval Based on Deep Convolutional NeuralNetworks and Binary Hashing Learning[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2068-2075. doi: 10.11999/JEIT151346

Citation:

PENG Tianqiang, LI Fang. Image Retrieval Based on Deep Convolutional NeuralNetworks and Binary Hashing Learning[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2068-2075. doi: 10.11999/JEIT151346

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Image Retrieval Based on Deep Convolutional NeuralNetworks and Binary Hashing Learning

doi: 10.11999/JEIT151346

PENG Tianqiang¹,
LI Fang²

1.
(Department of Computer Science and Engineering, Henan Institute of Engineering, Zhengzhou 451191, China)
2.
(Henan Image Recognition Engineering Center, Zhengzhou 450001, China)

Funds:

The National Natural Science Foundation of China (61301232)

Received Date: 2015-12-01
Rev Recd Date: 2016-04-29
Publish Date: 2016-08-19

Abstract

Abstract

With the increasing amount of image data, the image retrieval methods have several drawbacks, such as the low expression ability of visual feature, high dimension of feature, low precision of image retrieval and so on. To solve these problems, a learning method of binary hashing based on deep convolutional neural networks is proposed, which can be used for large-scale image retrieval. The basic idea is to add a hash layer into the deep learning framework and to learn simultaneously image features and hash functions should satisfy independence and quantization error minimized. First, convolutional neural network is employed to learn the intrinsic implications of training images so as to improve the distinguish ability and expression ability of visual feature. Second, the visual feature is putted into the hash layer, in which hash functions are learned. And the learned hash functions should satisfy the classification error and quantization error minimized and the independence constraint. Finally, an input image is given, hash codes are generated by the output of the hash layer of the proposed framework and large scale image retrieval can be accomplished in low-dimensional hamming space. Experimental results on the three benchmark datasets show that the binary hash codes generated by the proposed method has superior performance gains over other state-of-the-art hashing methods.
- Image retrieval,
- Deep convolutional neural networks,
- Binary hashing,
- Quantization error,
- Independence

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References(21)

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