A review of semantic segmentation using deep neural networks

By Yanming Guo, Y. Liu, Theodoros Georgiou, Michael S. Lew. 2017

851 citations recorded by the source corpus.

During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. Specifically, humans can perform image segmentation without even knowing what the objects are (for example, in satellite imagery or medical X-ray scans, there may be several objects which are unknown, but they can still be segmented within the image typically for further investigation). Performing segmentation without knowing the exact identity of all objects in the scene is an important part of our visual understanding process which can give us a powerful model to understand the world and also be used to improve or augment existing computer vision techniques. Herein this work, we review the field of semantic segmentation as pertaining to deep convolutional neural networks. We provide comprehensive coverage of the top approaches and summarize the strengths, weaknesses and major challenges.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations (2021, background): “Nowadays, deep learning architectures have achieved outstanding results in semantic segmentation [19, 21].”
Knowledge Distillation for Incremental Learning in Semantic Segmentation (2019, neutral): “Recent reviews on this topic are [18, 19, 20].”
Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, background): “For instance, convolutional neural networks (CNNs) are capable of learning low- and high-level features from raw images with stacks of convolutional and pooling layers, and then applying the extracted features to various computer vision tasks, such as large-scale image recognition [2], object detect”
Survey on semantic segmentation using deep learning techniques (2019, background): “Guo et al. [18] provided a review of semantic segmentation approaches, i.e., region-based, FCN-based and weakly supervised approaches.”
A large annotated medical image dataset for the development and evaluation of segmentation algorithms (2019, background): “Semantic segmentation algorithms are becoming increasingly general purpose and translatable to unseen tasks [2].”
A review of deep learning methods for semantic segmentation of remote sensing imagery (2021, background): “Guo et al. (2018) reviewed deep learning architectures for semantic segmentation of optical images and provided a categorical view of the existing methods.”