Matlab SIFT和HOG特征描述符

嘿，我要做分类。对于描述符，我计划使用LOWE的HOG和SIFT描述符

 1. For HOG, is that true that we need to compute the gradient of all image's pixels?? 
 For example we have image with size 10x10 pixels. And we compute the HOG to seek the orientation        
 for every pixels of image. And eventually we will get 100 orientations and generate histogram   
 (represent all of those image's pixels orientation). This Histogram is going to be used for the      
 classification??
 And to get the scale invariant we need to perform this descriptor of different size (scale)     
 images??

SIFT是尺度不变的特征变换。所以它是缩放和旋转不变的 我读到，在SIFT中，我们需要使用高斯平滑，以使图像平滑
分辨率变低

   2. Why we have to do that?
   And for scale invariant features, how to obtain that in SIFT?? Do we need to rescale our image   
   in every octave and then apply Gaussian filter in this new scaled images?? Or it's enough to   
   get scale invariant only in 1 octave by applying 3 times gaussian filter??
   How about the histogram, Is it same with HOG that we have to compute all the pixels???

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谢谢你，如果你打算实施SIFT，至少你应该读一下Lowe的论文，因为它是简历史上被引用最多的论文之一

在SIFT中，高斯平滑用于计算DOG（高斯差）。然后执行缩放极值检测，您将检测特征点

一旦有了这些特征点，就需要计算每个特征的HOG。你不需要为整个图像计算它！由于我们采用16x16邻域，因此结果将是128长度的描述符

比例不变性是因为梯度在每个关键点周围的窗口中相对于关键点的比例进行采样

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但再一次，你应该看一下Lowe的论文，因为它非常清楚。你还应该有一些清晰的概念，如特征点、猪、狗等。要真正理解SIFT

嗨，欢迎来到SO。你的问题除了格式不好之外，与编程无关。找出理论，然后带着可能遇到的任何实现问题回来。