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

Algorithm 1

Superpixel merging process.

Input: Data set, image-level label number .

Output: Cluster center for each target superpixel , the number of target superpixels in the image .

Step 1. SLIC superpixel segmentation,

Step 2. While

(a) Extract visual features of each superpixel: LAB(3 dim), Gabor(65 dim), Sift(64 dim), Surf(64 dim);

(b) The adjacency relationship between superpixels is counted and stored in matrix ;

(c) The superpixel similarity is calculated according to formula (1);

(d) Combine the most similar superpixel pairs with considering the adjacency;

(e) Calculate the mean of the merged superpixel clustering centers as a new clustering center;

(f) Update .

End

Step 3. Reclassify disconnected areas.