Weakly Supervised Deep Semantic Segmentation Using CNN and ELM with Semantic Candidate Regions

Algorithm 2

ELM classification algorithm.

Input: Given training samples , ; The number of semantic label categories ;

Activation function ; The number of hidden layer nodes is l , Test sample .

Output: Predicted result .

Step 1. Initialize the weight and bias between the input layer and the hidden layer, Randomly set

the value of and , given the value of .

Step 2. Select the activation function of the hidden layer and calculating the output matrix .

Step 3. Calculate the output weight of the network : (where is the transpose of ).

Step 4. The output weights of the test samples : .

Step 5. the output of the predicted result : .