图像中超像素的相邻和非相邻超像素 [英] Adjacent and non-adjacent superpixels for an superpixel in an image

问题描述

将图像分割成N个超像素后，我需要指定与一个超像素相邻或不相邻的超像素，并确定所有超像素的这种关系.

After segmenting an image into N superpixels, I need to specify the superpixels that are adjacent or non-adjacent to one superpixel and determine this relationship for all superpixels.

[L,NumLabels] = superpixels(A,200);

如何为每个超像素指定相邻的超像素?

How can I specify the adjacent superpixels for each of superpixels ?

更新

我尝试了@Cris Luengo引入的解决方案.但是出现了以下错误:

I have tried the solution @Cris Luengo introduced. However the following errors arised :

B=imread('H.jpg');
[L,N] = superpixels(B,200);
glcms=graycomatrix(L);
k=glcms(:,50);    %SupNum=50
[r,~]=find(k>0); 
aa=find(r==50);
r(aa)=[];

更新2 我按照MATLAB帮助中的说明进行操作，但对我不起作用. 对于SupNum = 8，产生了以下结果:

Update 2 I followed the instruction in MATLAB help but it doesn't work for me. For SupNum=8 the following result has produced:

推荐答案

在

In answers to this question on MATLAB Answers it is hinted that graycomatrix is a good way to solve this problem. However, those answers are incomplete.

graycomatrix需要几个参数来完成我们需要做的事情.它计算灰度值共生矩阵.这是一个矩阵，表示在单元格(i,j)中，灰度值i与另一个灰度值j相邻出现的频率.可以在此函数中定义下一个"关系.默认情况下，graycomatrix返回一个8x8矩阵，它将矩阵中的所有灰度值归为8个bin，并查找组i中任何出现在组j中任何灰度值旁边的灰度值.

graycomatrix requires several arguments to do what we need it to do. It computes a gray-value co-occurrence matrix. This is a matrix that says, in cell (i,j), how often a gray-value i occurs next to another gray-value j. The "next to" relationship can be defined in this function. By default, graycomatrix returns an 8x8 matrix, where it bins all gray-values in the image into 8 bins, and looks for any gray-value in group i occurring next to any gray-value in group j.

因此，我们需要在此共生矩阵中将超像素图像中的每个标签都分开(有N个不同的标签或灰度值).我们还需要将"next to"关系指定为[1,0]或[0,1]，即水平或垂直彼此相邻的两个像素.当指定两个相邻"关系时，我们以3D矩阵的形式返回两个共现矩阵.还要注意，共生矩阵不是对称的，在我们的超像素图像中，标签i可能会出现在标签j的左边，但是在那种情况下j也不太可能也会出现在j的左边c3>.因此，glcms(i,j)的计数将为非零，而glcms(j,i)的计数将为零.在下面的代码中，我们通过显式使矩阵对称来克服此问题.

So we need to keep each label in our superpixel image separate in this co-occurrence matrix (there are N different labels, or gray-values). We also need to specify the "next to" relationship to be either [1,0] or [0,1], i.e. two pixels next to each other horizontally or vertically. When specifying two "next to" relations, we get two co-occurrence matrices back, in the form of a 3D matrix. Note also that the co-occurrence matrix is not symmetric, in our superpixel image, label i might happen to the left of label j, but in that case it is unlikely that j also happens to the left of i. Therefore, glcms(i,j) would have a non-zero count, but glcms(j,i) would be zero. In the code below we overcome this by explicitly making the matrix symmetric.

这是代码:

B = imread('kobi.png'); % using one of MATLAB's standard images
[L,N] = superpixels(B,200);
glcms = graycomatrix(L,'NumLevels',N,'GrayLimits',[1,N],'Offset',[0,1;1,0]);
glcms = sum(glcms,3);    % add together the two matrices
glcms = glcms + glcms.'; % add upper and lower triangles together, make it symmetric
glcms(1:N+1:end) = 0;    % set the diagonal to zero, we don't want to see "1 is neighbor of 1"

glcms现在是邻接矩阵.如果超像素i和j是相邻像素，则glcms(i,j)处的值不为零.该值指示两个超像素之间的边界有多大.

glcms is now the adjacency matrix. The value at glcms(i,j) is non-zero if superpixels i and j are neighbors. The value indicates how large the boundary between the two superpixels is.

要计算邻接表:

[I,J] = find(glcms);     % returns coordinates of non-zero elements
neighbors = [J,I]

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