使用K均值聚类(使用RGB特征)对图像进行颜色量化
sel*_*cia 3 matlab image image-processing k-means
是否可以使用matlab对图像的RGB +空间特征进行聚类？
注意:我想kmeans用于群集.
事实上,基本上我想做一件事,我想得到这个形象
由此
我想你正在寻找颜色量化.
[imgQ,map]= rgb2ind(img,4,'nodither'); %change this 4 to the number of desired colors
%in quantized image
imshow(imgQ,map);
结果:
使用kmeans:
%img is the original image
imgVec=[reshape(img(:,:,1),[],1) reshape(img(:,:,2),[],1) reshape(img(:,:,3),[],1)];
[imgVecQ,imgVecC]=kmeans(double(imgVec),4); %4 colors
imgVecQK=pdist2(imgVec,imgVecC); %choosing the closest centroid to each pixel,
[~,indMin]=min(imgVecQK,[],2); %avoiding double for loop
imgVecNewQ=imgVecC(indMin,:); %quantizing
imgNewQ=img;
imgNewQ(:,:,1)=reshape(imgVecNewQ(:,1),size(img(:,:,1))); %arranging back into image
imgNewQ(:,:,2)=reshape(imgVecNewQ(:,2),size(img(:,:,1)));
imgNewQ(:,:,3)=reshape(imgVecNewQ(:,3),size(img(:,:,1)));
imshow(img)
figure,imshow(imgNewQ,[]);
结果kmeans:
![通过<code>kmeans</code>,代码将略有不同.基本上,您还需要连接相应像素值的像素坐标.但请记住,在为每个像素指定最近的质心时,只分配颜色,即前3个维度,而不是最后2个维度.显然,这没有意义.代码与之前的代码非常相似,请注意更改并理解它们.</p>
<pre><code>[col,row]=meshgrid(1:size(img,2),1:size(img,1));
imgVec=[reshape(img(:,:,1),[],1) reshape(img(:,:,2),[],1) reshape(img(:,:,3),[],1) row(:) col(:)];
[imgVecQ,imgVecC]=kmeans(double(imgVec),4); %4 colors
imgVecQK=pdist2(imgVec(:,1:3),imgVecC(:,1:3));
[~,indMin]=min(imgVecQK,[],2);
imgVecNewQ=imgVecC(indMin,1:3); %quantizing
imgNewQ=img;
imgNewQ(:,:,1)=reshape(imgVecNewQ(:,1),size(img(:,:,1))); %arranging back into image
imgNewQ(:,:,2)=reshape(imgVecNewQ(:,2),size(img(:,:,1)));
imgNewQ(:,:,3)=reshape(imgVecNewQ(:,3),size(img(:,:,1)));
imshow(img)
figure,imshow(imgNewQ,[]);
</code></pre><a target=](https://i.stack.imgur.com/ebUE4.jpg)
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kmeans距离约束的结果:
