1. Generate superpixel maps using K means clustering.

2. Generate Superpixel maps using SLIC algorithm.

3. Build a segmentation and classification network by training a deep neural network on the superpixels. To achieve this the following steps has to be taken:

   a. Dilate and save each superpixel region from SLIC output into image of size 224X224, alongwith the ground truth segments label. b. Build a couple of convolution layers to extract the deep features from each Superpixel patch image. Let the last layer be Fully connected layers. c. Define the segmentation loss as multi-class classification loss and train a convolutional neural network based classifier.

Then, during inference, we have to combine the classifier's predicted labels to form the whole input image's Superpixel segmentation results.