什么是“语义分割"?与“分段"相比和“场景标记"? [英] What is "semantic segmentation" compared to "segmentation" and "scene labeling"?
问题描述
语义分割只是一个Pleonasm还是语义分割"和分割"之间有区别?场景标注"和场景解析"有区别吗?
像素级分割和像素级分割有什么区别?
(附带问题:当您有这种像素级注释时，您是免费获得对象检测还是还有其他事情要做?)
请给出您的定义的来源.
使用语义分割"的来源
- Jonathan Long、Evan Shelhamer、Trevor Darrell:解决方案
segmentation" 是将图像分割成几个连贯"的部分，但没有任何尝试理解这些部分代表什么.最著名的作品之一(但绝对不是第一部)是 Shi 和马利克标准化剪切和图像分割"PAMI 2000.这些作品试图根据颜色、纹理和边界平滑度等低级线索来定义连贯性".您可以将这些作品追溯到格式塔理论.
另一方面，语义分割"尝试将图像划分为具有语义意义的部分，并且将每个部分分类为一个预先确定的类别.您还可以通过对每个像素(而不是整个图像/片段)进行分类来实现相同的目标.在这种情况下，您正在进行逐像素分类，这会导致相同的最终结果，但路径略有不同......
所以，我想你可以说语义分割"、场景标记"和逐像素分类"基本上都在努力实现相同的目标:从语义上理解图像中每个像素的作用.您可以通过多种途径来实现该目标，而这些途径会导致术语上的细微差别.
Is semantic segmentation just a Pleonasm or is there a difference between "semantic segmentation" and "segmentation"? Is there a difference to "scene labeling" or "scene parsing"?
What is the difference between pixel-level and pixelwise segmentation?
(Side-question: When you have this kind of pixel-wise annotation, do you get object detection for free or is there still something to do?)
Please give a source for your definitions.
Sources which use "semantic segmentation"
- Jonathan Long, Evan Shelhamer, Trevor Darrell: Fully Convolutional Networks for Semantic Segmentation. CVPR, 2015 and PAMI, 2016
- Hong, Seunghoon, Hyeonwoo Noh, and Bohyung Han: "Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation." arXiv preprint arXiv:1506.04924, 2015.
- V. Lempitsky, A. Vedaldi, and A. Zisserman: A pylon model for semantic segmentation. In Advances in Neural Information Processing Systems, 2011.
Sources which use "scene labeling"
- Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun: Learning Hierarchical Features for Scene Labeling. In Pattern Analysis and Machine Intelligence, 2013.
Source which use "pixel-level"
- Pinheiro, Pedro O., and Ronan Collobert: "From Image-level to Pixel-level Labeling with Convolutional Networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015. (see http://arxiv.org/abs/1411.6228)
Source which use "pixelwise"
- Li, Hongsheng, Rui Zhao, and Xiaogang Wang: "Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification." arXiv preprint arXiv:1412.4526, 2014.
Google Ngrams
"Semantic segmentation" seems to be more used recently than "scene labeling"
解决方案"segmentation" is a partition of an image into several "coherent" parts, but without any attempt at understanding what these parts represent. One of the most famous works (but definitely not the first) is Shi and Malik "Normalized Cuts and Image Segmentation" PAMI 2000. These works attempt to define "coherence" in terms of low-level cues such as color, texture and smoothness of boundary. You can trace back these works to the Gestalt theory.
On the other hand "semantic segmentation" attempts to partition the image into semantically meaningful parts, and to classify each part into one of the pre-determined classes. You can also achieve the same goal by classifying each pixel (rather than the entire image/segment). In that case you are doing pixel-wise classification, which leads to the same end result but in a slightly different path...
So, I suppose you can say that "semantic segmentation", "scene labeling" and "pixelwise classification" are basically trying to achieve the same goal: semantically understanding the role of each pixel in the image. You can take many paths to reach that goal, and these paths lead to slight nuances in the terminology.
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