Chaowen Lu1,a, Feifei Lee1,a,*, Lei Chen1, Sheng Huang1, Qiu Chen2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 593-608, 2019, DOI:10.32604/cmes.2019.07796
Abstract Image retrieval has become more and more important because of the explosive
growth of images on the Internet. Traditional image retrieval methods have limited image
retrieval performance due to the poor image expression abhility of visual feature and
high dimension of feature. Hashing is a widely-used method for Approximate Nearest
Neighbor (ANN) search due to its rapidity and timeliness. Meanwhile, Convolutional
Neural Networks (CNNs) have strong discriminative characteristics which are used for
image classification. In this paper, we propose a CNN architecture based on improved
deep supervised hashing (IDSH) method, by which the binary compact More >
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