Abstract
Image segmentation is a very important topic in the field of computer vision. We present a method for semantic segmentation of selected stuff classes from a superset of classes. We show that in situations where only select stuff classes are required if we group them as per a strategy then it can attain much higher accuracy than the models trained on the original dataset with all classes intact. The COCO-Stuff Dataset is used for demonstrating the aforesaid strategy. For training purposes, the DeepLabv3+ with Mobilenet-v2 architecture is used. We have achieved an 80.2 percent mean Intersection over Union (mIoU) on these selected classes. We also refine the masks using Learning/Computer Vision (CV) methods and hence obtain better visualization results as compared to the existing DeepLabv3+ results.
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