July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Content preview from 深度学习入门 : 基于Python的理论与实现
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8.4 深度学习的应用案例
255
8.4.2
图像分割
图像分割是指在像素水平上对图像进行分类。如图8
-
19 所示，使用以像
素为单位对各个对象分别着色的监督数据进行学习。然后，在推理时，对输
入图像的所有像素进行分类。
图8-19 图像分割的例子（引用自文献[34]）：左边是输入图像，右边是监督用的带标签图像
之前实现的神经网络是对图像整体进行了分类，要将它落实到像素水平
的话，该怎么做呢？
要基于神经网络进行图像分割，最简单的方法是以所有像素为对象，对
每个像素执行推理处理。比如，准备一个对某个矩形区域中心的像素进行分
类的网络，以所有像素为对象执行推理处理。正如大家能想到的，这样的
方法需要按照像素数量进行相应次
forward
处理，因而需要耗费大量的时间
（正确地说，卷积运算中会发生重复计算很多区域的无意义的计算）。为了解
决这个无意义的计算问题，有人提出了一个名为
FCN
（
Fully Convolutional
Network
）
[37]
的方法。该方法通过一次
forward
处理，对所有像素进行分类（图
8
-
20）。
FCN
的字面意思是 “全部由卷积层构成的网络”。相对于一般的
CNN
包
含全连接层，
FCN
将全连接层替换成发挥相同作用的卷积层。在物体识别
中使用的网络的全连接层中，中间数据的空间容量被作为排成一列的节点进
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