Image (color?) segmentation with opencv C++

Question

As the graph showed, I'd like to input image and get several segments as a result like that.

It's just like cluster the closest color segment, so I think it's close to the concept of "meanshift"?

I've searched relevant questions here but still don't know how to start and construct the structure in opencv C++. I'm looking for some advises, and I'll be very appreciate if getting a piece of implementation code for me to reference! Thanks for any help!!

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Edit 5/19/2015

Let me add that one of my trying implementations is Watershed here:(http://blog.csdn.net/fdl19881/article/details/6749976).

It's not perfect but the result i want. In this implement, user needs to operate manually( draw the watershed lines ), so i'm looking for AUTOMATIC version of it. Sounds a little bit hard, but... i'll appreciate for some suggestion or piece of code to do it.

Answer 1

Opencv Documentation: Link

Parameters: here

Sample code for Meanshift filtering:

#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;
using namespace std;

Mat img, res, element;

int main(int argc, char** argv)
{

   namedWindow( "Meanshift", 0 );
   img = imread( argv[1] );
// GaussianBlur(img, img, Size(5,5), 2, 2);
   pyrMeanShiftFiltering( img, res, 20, 45, 3);
   imwrite("meanshift.png", res);
   imshow( "Meanshift", res );

   waitKey();

   return 0;
}

This is the output with your image, you might need to use some pre-processing before or maybe find some better parameters:

EDIT: Output with some gaussian blur beforehand (comment in code)

Answer 2

The problem with looking at existing segmentation approaches is that they are either implemented in Matlab (which nobody outside of Uni can use) or they are not automatic. An approach where the user needs to preprocess the picture by choosing objects of interest or levels that indicate how to split colors is not useful because it is not automatic. If you like, you can try my OpenCV based implementation of segmentation described in this blog post. It is not perfect, but it is automatic and does most of the job and you can actually download the source and try it out.