特征检测和描述符提取之间有什么区别? [英] What is the difference between feature detection and descriptor extraction?

问题描述

有人知道OpenCV 2.3中特征检测和描述符提取之间的区别吗?

我知道使用DescriptorMatcher进行匹配时需要后者.如果是这种情况，FeatureDetection的用途是什么?

解决方案

功能检测

• 在计算机视觉和图像处理中，特征检测的概念指的是旨在计算图像信息的抽象并在每个图像点上做出本地决策的方法，该点是否存在给定类型的图像特征或不是.生成的特征将是图像域的子集，通常以孤立点，连续曲线或连接区域的形式出现.

  特征检测=如何在图像中找到一些有趣的点(特征). (例如，找到一个角落，找到一个模板，依此类推.)

特征提取

• 在模式识别和图像处理中，特征提取是降维的一种特殊形式.当算法的输入数据太大而无法处理并且被怀疑是多余的(大量数据，但没有太多信息)时，输入数据将被转换为特征的简化表示集(也称为特征向量) .将输入数据转换为特征集称为特征提取.如果精心选择了提取的特征，则可以预期特征集将从输入数据中提取相关信息，以便使用此缩小的表示形式而不是完整尺寸的输入来执行所需的任务.

  特征提取=如何表示有趣的点，我们将它们与图像中的其他有趣的点(特征)进行了比较. (例如，该点的局部强度?该点周围区域的局部方向?等等)

实际示例:您可以使用harris角点方法找到一个角点，但是可以使用任何所需的方法来描述它(例如直方图，HOG，第8个邻接点中的局部方向)

您可以在此Wikipedia文章中看到更多信息.

Does anyone know the difference between feature detection and descriptor extraction in OpenCV 2.3?

I understand that the latter is required for matching using DescriptorMatcher. If that's the case, what is FeatureDetection used for?

解决方案

Feature detection

• In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions.

  Feature detection = how to find some interesting points (features) in the image. (For example, find a corner, find a template, and so on.)

Feature extraction

• In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (much data, but not much information) then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full-size input.

  Feature extraction = how to represent the interesting points we found to compare them with other interesting points (features) in the image. (For example, the local area intensity of this point? The local orientation of the area around the point? And so on)

Practical example: You can find a corner with the harris corner method, but you can describe it with any method you want (Histograms, HOG, Local Orientation in the 8th adjacency for instance)

You can see here some more information in this Wikipedia article.

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