在VLFeat Bag-of-Words(鞠躬)

I'm building a project Images Classification with Bag-of-Visual-Words (BoVW) using VLFeat library. The BoVW pipeline includes:

我正在使用VLFeat库构建一个项目图像分类，其中包含了visualword (BoVW)。BoVW管道包括:

SIFT
筛选
k-means
k - means
Building histogram
建立柱状图
SVM classification
支持向量机分类

I can use vl_sift and vl_kmeans for (1) and (2), but I don't know how to build histogram features and use them in SVM.

我可以对(1)和(2)使用vl_sift和vl_kmeans，但是我不知道如何构建直方图特性并在SVM中使用它们。

1 个解决方案

#1

given that you already have the "dictionary" from vl_kmeans:

既然你已经有了vl_kmeans的“字典”:

[centers] = vl_kmeans(data, numClusters);

In order to build histogram of image I, you need to get the 128-D descriptors of that image using vl_sift:

为了构建image I的直方图，您需要使用vl_sift来获取该图像的128-D描述符:

[~,D] = vl_sift(I)

Each column of D is the descriptor of one interest point (or frame) in image I. Now you need to build the histogram of I based on D and the dictionary centers. The simplest way is using a for loop:

D的每一列都是图像I中一个兴趣点(或帧)的描述符。现在需要基于D和字典中心构建I的直方图。最简单的方法是使用for循环:

H = zeros(1,numClusters);
for i=1:size(D,2)
  [~, k] = min(vl_alldist(D(:,i), centers)) ;
  H(k) = H(k) + 1;
end

Now it is up to you to normalise the histogram H or not, before passing it to SVM. Note that there is possibly a faster way to build the histogram that does not need a loop; but I think my code (in Matlab) is clear enough to explain the algorithm.

现在，在将直方图传递给SVM之前，是否将其正常化取决于您。注意，可能有一种更快的方法来构建不需要循环的直方图;但是我认为我的代码(在Matlab中)足够清晰，可以解释这个算法。

#1