مقاله انگلیسی رایگان در مورد یک نظرسنجی در مورد تکنیک های یادگیری عمیق برای بخش بندی معنایی تصویر و ویدئو – الزویر 2018

1- Introduction

2- Terminology and background concepts

3- Materials and methods

4- Discussion

5- Conclusion

References

Abstract

Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we formulate the semantic segmentation problem and define the terminology of this field as well as interesting background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review common loss functions and error metrics for this problem. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.

Introduction

Nowadays, semantic segmentation – applied to still 2D images, video, and even 3D or volumetric data – is one of the key problems in the field of computer vision. Looking at the big picture, semantic segmentation is one of the high-level tasks that paves the way toward complete scene understanding. The importance of scene understanding as a core computer vision problem is highlighted by the fact that an increasing number of applications nourish from inferring knowledge from imagery. Some of those applications include autonomous driving [1–3], human-machine interaction [4], computational photography [5], image search engines [6], and augmented reality to name a few. Such problem has been addressed in the past using various traditional computer vision and machine learning techniques. Despite the popularity of those kind of methods, the deep learning revolution has turned the tables so that many computer vision problems – semantic segmentation among them – are being tackled using deep architectures, usually Convolutional Neural Networks (CNNs) [7–11], which are surpassing other approaches by a large margin in terms of accuracy and sometimes even efficiency. However, deep learning is far from the maturity achieved by other old-established branches of computer vision and machine learning. Because of that, there is a lack of unifying works and state of the art reviews. The ever-changing state of the field makes initiation difficult and keeping up with its evolution pace is an incredibly time-consuming task due to the sheer amount of new literature being produced. This makes it hard to keep track of the works dealing with semantic segmentation and properly interpret their proposals, prune subpar approaches, and validate results. To the best of our knowledge, this is the first review to focus explicitly on deep learning for semantic segmentation. Various semantic segmentation surveys already exist such as the works by Zhu et al. [12] and Thoma [13], which do a great work summarizing and classifying existing methods, discussing datasets and metrics, and providing design choices for future research directions. However,they lack some ofthemost recent datasets,they donot analyze frameworks, and none of them provide details about deep learning techniques. Because of that, we consider our work to be novel and helpful thus making it a significant contribution for the research community.