图像处理(算法或描述)中的特征描述符是什么? [英] What is a feature descriptor in image processing (algorithm or description)?

问题描述

在图像特征的上下文中，我经常与术语描述符的含义混淆.描述符是对点的局部邻域的描述(例如，浮点矢量)，还是描述符是输出描述的算法?另外，特征提取器的输出到底是什么?

I get often confused with the meaning of the term descriptor in the context of image features. Is a descriptor the description of the local neighborhood of a point (e.g. a float vector), or is a descriptor the algorithm that outputs the description? Also, what exactly is then the output of a feature-extractor?

我已经问了很长时间这个问题，而我唯一的解释是描述符既是算法又是描述.特征检测器用于检测独特点.但是，特征提取器似乎没有任何意义.

I have been asking myself this question for a long time, and the only explanation I came up with is that a descriptor is both, the algorithm and the description. A feature detector is used to detect distinctive points. A feature-extractor, however, does then not seem to make any sense.

那么，特征描述符是描述还是生成描述的算法?

So, is a feature descriptor the description or the algorithm that produces the description?

推荐答案

特征检测器是一种算法，它获取图像并输出位置(即像素坐标)图片中的重要区域.一个示例是角检测器，它输出图像中角的位置，但不输出告诉您有关检测到的功能的任何其他信息.

A feature detector is an algorithm which takes an image and outputs locations (i.e. pixel coordinates) of significant areas in your image. An example of this is a corner detector, which outputs the locations of corners in your image but does not tell you any other information about the features detected.

特征描述符是一种算法，它获取图像并输出特征描述符/特征向量.特征描述符将有趣的信息编码为一系列数字，并充当一种数字指纹"，可用于将一个特征与另一个特征区分开.理想情况下，此信息在图像变换下将是不变的，因此即使以某种方式对图像进行变换，我们也可以再次找到该特征.一个示例是 SIFT ，该编码对有关本地邻域图像渐变的信息进行编码.特征向量.您可以阅读的其他示例是 HOG 和

A feature descriptor is an algorithm which takes an image and outputs feature descriptors/feature vectors. Feature descriptors encode interesting information into a series of numbers and act as a sort of numerical "fingerprint" that can be used to differentiate one feature from another. Ideally this information would be invariant under image transformation, so we can find the feature again even if the image is transformed in some way. An example would be SIFT, which encodes information about the local neighbourhood image gradients the numbers of the feature vector. Other examples you can read about are HOG and SURF.

当涉及到特征探测器时，位置"还可能包含描述特征尺寸或比例的数字.这是因为放大"时看起来像角的东西在缩小"时看起来不像角，因此指定比例信息很重要.因此，您可能不仅仅使用(x，y)对作为图像空间"中的位置，还可以使用三重(x，y，scale)作为位置比例空间".

When it comes to feature detectors, the "location" might also include a number describing the size or scale of the feature. This is because things that look like corners when "zoomed in" may not look like corners when "zoomed out", and so specifying scale information is important. So instead of just using an (x,y) pair as a location in "image space", you might have a triple (x,y,scale) as location in "scale space".

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