Sehar Uroosa, Naseem Muhammad Luqman
University of Engineering and Technology, Taxila, Pakistan.
Northeastern University, Shenyang, China.
Multimed Tools Appl. 2022;81(21):30519-30544. doi: 10.1007/s11042-022-12821-3. Epub 2022 Apr 6.
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.
语义分割涉及从图像中提取有意义的信息，或从视频或录制帧中提取输入信息。它是通过使用分类方法逐像素检查来执行提取的方式。它能从我们进一步评估所需的数据中为我们提供更准确、更精细的细节。以前，我们有一些基于无监督学习观点或一些传统方法的技术来执行一些图像处理任务。随着时间的推移，技术不断进步，现在我们有了更先进、更高效的分割方法。从技术角度来看，图像分割比语义分割稍微简单一些，因为语义分割是基于像素的。之后，基于标签检测到的部分将被遮罩，并根据我们用相关类名和指定颜色定义的类别来指代被遮罩的对象。在本文中，我们从头开始回顾了几乎所有用于语义分割的监督学习和无监督学习算法，直至先进且更高效的算法。就深度学习而言，到目前为止我们已经开发了许多技术。在这个研究领域，我们研究了大约120篇论文。我们总结了深度学习如何帮助解决语义分割的关键问题，并为我们提供更高效的结果。我们回顾并全面研究了关于语义分割的不同综述，特别是使用深度学习的综述。
