SIFT和HOG特征描述符

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Heyyy ,,,我要做分类。而对于描述我计划使用HOG和LOWE SIFT描述..SIFT和HOG特征描述符

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是尺度不变特征变换。所以它是比例和旋转不变量。
我读from here，在SIFT我们需要做的采用高斯，使我们的图像的
分辨率变低平滑..

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???

Thankss

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2014-04-03 user3378327

嗨，欢迎来到SO。除了格式不正确之外，你的问题与编程无关。找出理论并回来，可能遇到任何实施问题。 –

如果您打算实现SIFT至少你应该阅读Lowe以来的论文是CV历史上引用最多的一篇！

在SIFT中使用高斯平滑来计算DOG（高斯差）。然后执行Scale Extrema Detection，您将检测特征点。

一旦你有了这个特征点，你将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的邻域，结果将是一个128长度的描述符。

尺度不变性是因为梯度在每个关键点周围的窗口中针对按键的尺度进行采样。

但是，你应该再次通过Lowe的论文，因为它在那里非常清楚。你还应该有一些清晰的概念，比如特征点，HOG，DOG等等，才能真正理解SIFT

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2014-04-05 15:07:23

SIFT和HOG特征描述符

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