How deep learning is empowering semantic segmentation

Abstract

Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. It is the way to perform the extraction by checking pixels by pixel using a classification approach. It gives us more accurate and fine details from the data we need for further evaluation. Formerly, we had a few techniques based on some unsupervised learning perspectives or some conventional ways to do some image processing tasks. With the advent of time, techniques are improving, and we now have more improved and efficient methods for segmentation. Image segmentation is slightly simpler than semantic segmentation because of the technical perspective as semantic segmentation is pixels based. After that, the detected part based on the label will be masked and refer to the masked objects based on the classes we have defined with a relevant class name and the designated color. In this paper, we have reviewed almost all the supervised and unsupervised learning algorithms from scratch to advanced and more efficient algorithms that have been done for semantic segmentation. As far as deep learning is concerned, we have many techniques already developed until now. We have studied around 120 papers in this research area. We have concluded how deep learning is helping in solving the critical issues of semantic segmentation and gives us more efficient results. We have reviewed and comprehensively studied different surveys on semantic segmentation, specifically using deep learning.

Semantic Image Segmentation Using Scant Pixel Annotations

“…Semantic segmentation [4] is one of the challenging image analyses tasks that has been studied earlier using image processing algorithms and more recently using deep learning networks; see [6,10,11,18] for detailed surveys. Several image processing algorithms based on methods including clustering, texture and color filtering, normalized cuts, superpixels, graph and edge-based region merging, have been developed to perform segmentation by grouping similar pixels and partitioning a given image into visually distinguishable regions [6].…”

“…Currently, deep learning-based approaches are perhaps the de facto choice for semantic segmentation. Recently, Sehar and Naseem [11], reviewed most of the popular learning algorithms (∼120) for semantic segmentation tasks, and concluded the overwhelming success of deep learning compared to the classical learning algorithms. However, as pointed out by the authors, the need for large volumes of training data is a well-known problem in developing segmentation models using deep networks.…”

“…Recent approaches have used convolutional neural networks (CNNs) [1,2] and fully convolutional networks (FCNs) [3][4][5] for this task and achieved promising results. Several recent surveys [6][7][8][9][10][11] describe the successes of semantic image segmentation and directions for future research.…”

“…Now consider the pixels that are not in Target. The pixel-level labels for these pixels in t p can be decided using either the previous model M i−1 or the current model M i (lines [11][12][13][14][15][16][17]. Let p xy be a pixel in x th row and y th column of t p .…”

MBMT-Net: A Multi-Task Learning Based Convolutional Neural Network Architecture for Dense Prediction Tasks

“…It requires a model to process an input image, and assign each pixel the semantic class it belongs to. Mainly, it is used to classify regions, and understand the scene, so that other systems could make decisions based on the findings [12], [13].…”