April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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自动特征生成：图像特征提取和深度学习
｜
109
在过去
20
年中，计算机视觉研究的重点是人工定义的用于提取良好图像特征的流程。有
一段时间，像
SIFT
和
HOG
（后文中会介绍）这样的图像特征提取器曾经成为了标准。近
期深度学习研究的发展扩展了传统机器学习模型的应用范围，它们在基础层中集成了自动
特征提取技术。这种扩展实质上是使用人工定义的能自动学习和提取特征的模型代替人工
定义的特征图像提取器。人工工作依然存在，只是更加深入地抽象到了建模过程内部。
在本章中，我们从最流行的图像特征提取器开始，然后详细介绍本书中最为复杂的建模机
制——用于特征学习的深度学习。
8.1
最简单的图像特征
（
以及它们因何失效
）
从图像中提取出的
正确
特征是什么？答案当然取决于特征的用途。假设我们的任务是图像
检索：给定一张图片，我们要从一个图像数据库中找出与其相似的图片。我们需要确定如
何表示每张图像，以及如何测量图像之间的区别。我们能只考虑不同颜色在图像中的比例
吗？图
8-1
中给出了两张图片，它们具有大致相同的颜色配置，但是含义则大相径庭。一
张似乎是蓝天上的白云，另一张则是海上的帆船。因此，仅凭颜色信息可能还不足以标识
一幅图像。
图 8-1：蓝色与白色组成的图片——同样的颜色配置，完全不同的意义
另外一种简单思想是测量图像之间的像素值差异。首先，调整图像，使它们具有同样的宽
度和高度。每幅图像都用一个像素值矩阵来表示，这个矩阵可以按行或按列堆叠成一个长
向量。每个像素的颜色（如颜色的
RGB
编码）就是图像的一个特征。最后，测量出长像
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.