Different Approaches for Semantic Segmentation

Abstract

Semantic image segmentation is an emerging task in the field of automation. Its application varies from autonomous driving to medical diagnosis. Semantic segmentation of an image means to label each pixel in that image to a particular class. As an example consider an outdoor street image where there are different objects like car, road, sky, trees, pedestrians etc. After applying semantic segmentation each pixel in the image belonging to the car will have the label car and road will have label road and so on. A recent trend in performing semantic segmentation is by using Convolutional Neural Networks, (CNN), which acted as a catalyst for segmentation. In this paper, a detailed discussion of various approaches for segmentation using CNN has been presented. Also, various datasets and their format and evaluations metrics are discussed. All the approaches discussed are diverse and has its pros and cons. Finally, an application-specific semantic segmentation method using a genetic CNN algorithm for classification task has been proposed. The proposed method has shown improvement in the M iou score when tested on the CamVid dataset and on a dataset created by combining two small object classification datasets, MNIST and CIFAR10.