Computational Intelligence and Neuroscience / 2019 / Article / Alg 1

Research Article

Triplet Deep Hashing with Joint Supervised Loss Based on Deep Neural Networks

Algorithm 1

The procedure of JLTDH.
Input: training images ; code length ; epochs = 150; superparameters ; and minibatch = 128
Initialization: initialize neural network parameters , , and with the AlexNet model and iteration number
 Generate triplet training set: ;
for ; ; do
 Randomly sample a minibatch of points from , and for each sampled point :
(i)Calculate by forward propagation;
(ii)Compute ;
(iii)Compute the binary code of with ;
(iv)Compute derivatives for point ;
(v)Update the parameters by utilizing BP;
(vi)Compute ;
for ; ; do
  Discrete cyclic coordinate descent (DCC) optimization:
(i)Compute according to (16);
(ii)Iteratively optimization update bit by bit using the DCC method according to (21) in the minibatch;
End for
 Update in the minibatch according to joint loss function in (13);
End for
Output: , with the parameters ;
 Hash codes .

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