Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation

By Hongyuan Zhu, Fanman Meng, Jianfei Cai, Shijian Lu. 2015

238 citations recorded by the source corpus.

Abstract Image segmentation refers to the process to divide an image into meaningful non-overlapping regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. While many segmentation algorithms exist, there are only a few sparse and outdated summarizations available. Thus, in this paper, we aim to provide a comprehensive review of the recent progress in the field. Covering 190 publications, we give an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi-supervised methods and the fully-supervised methods. In addition, we review the existing influential datasets and evaluation metrics. We also suggest some design choices and research directions for future research in image segmentation.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks (2019, methodology): “2006; Karpathy, Miller, and Fei-Fei 2013), as well as work on co-segmentation (Zhu et al. 2016) and unsupervised object recognition (Tuytelaars et al.”
A Review on Deep Learning Techniques Applied to Semantic Segmentation (2017, methodology): “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 meth-ods, discussing datasets and metrics, and providing design choices for future research directions.”
Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges (2017, background): “A related survey in [16] on semantic segmentation literature is presented.”
A survey on deep learning techniques for image and video semantic segmentation (2018, methodology): “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.”
Survey on semantic segmentation using deep learning techniques (2019, background): “A Survey by Zhu et al. [15] covering a wide range of the papers and areas of semantic segmentation topics including, interactive methods, recent development in the super-pixel, object proposals, semantic image parsing, image co-segmentation, semi & weakly supervised, and fully supervised image…”
FSDR: Frequency Space Domain Randomization for Domain Generalization (2021, background): “Deep learning based methods [4, 38, 79, 84] have achieved great successes at the price of large-scale densely-annotated training data [8] that are usually prohibitively expensive and timeconsuming to collect.”