如何使用SIFT / SURF作为机器学习算法的功能？ [英] How to use SIFT/SURF as features for a machine learning algorithm?

问题描述

我正在处理一个自动图像注释问题，我试图将标签与图像相关联。为此我尝试用SIFT功能进行学习。但问题是所有SIFT功能都是一组关键点，每个关键点都有一个二维数组，关键点的数量也很大。我的学习算法如何通常只接受一个-d features？

Im working on an automatic image annotation problem in which im trying to associate tags with images. For that im trying for SIFT features for learning. But the problem is all the SIFT features are a set of keypoints, each of which have a 2-D array, and the number of keypoints are also huge.How many and how do I give them for my learning algorithm which typically accepts only one-d features?

推荐答案

您可以将单个SIFT表示为可视单词，这是一个数字并将其用作SVM输入，我认为这是你需要的。它通常通过k-means聚类来完成。

You can represent single SIFT as "visual word" which is one number and use it as SVM input, I think it is what you need. It is usually done by k-means clustering.

这种方法被称为词袋，并在本文。

This method is called "bag-of-words" and described in this paper.

< a href =http://www.robots.ox.ac.uk/~az/icvss08_az_bow.pdf =nofollow>简短的方法评论。

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