Tongue Images Classification Based on Constrained High Dispersal Network

Algorithm 1

Tongue images classification based on CHDNet.

Input: Tongue images with labels . where

.

Output: Predict labels of testing images.

Partition the whole dataset into training set and testing set.

  if training set then

 Compute patch mean removal of .

 for to stage  do

  Compute convolutional kernels at stage .

  Compute convolutional feature map using (2).

  Compute non-linear transformation feature map

     using (3).

  Compute the convolutional kernels at stage .

  if ==stage then

   for each convolutional filter do

    Compute the multi-scale feature maps using (9).

    Apply high dispersal operation by (10).

    Execute local response normalization by (11).

    Compute the feature map at stage according

         to (12).

   end for

    end if

 end for

 Extract feature representations for by (6).

  else

(20)  for to stage do

    Compute the feature map at stage using the learned

      kernels, which is similar to Steps .

 end for

 Extract multiscale features , which is similar to

     Step .

  end if

  for validation = 1 to do

 Train classifier:

      .

 Predict labels for test images:

  end for