IMPLEMENTATION OF MEAN SHIFT IMAGE SEGMENTATION IN PYTHON
Author(s): Pranshu Gupta, Abhishek Jain
INDIAN INSTITUTE OF TECHNOLOGY KANPUR
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The description of our implementation is as follows:
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We load the image using the 'Image' module of python. It gives us a matrix of RGB values. 
The shape of this matrix is same as the resolution of the image.
We pick one initial seed for mean shift filtering at every 40th pixel in height and width. Then we 
run the mean shift algorithm serially for each of these seeds. We iterate atmost 10 times at each 
seed for it to converge, otherwise we move on to the next seed.
We have implemented Flat and Gaussian Kernel for Mean Shift Algorithm, the user has to choose one 
them at the run time by supplying a command line argument. (0 for flat and 1 for gaussian)
After we get the converged seeds, we group together the seeds which are closer than the bandwidth. 
We do this by taking their average vector.
Then we find out the nearest seed for each of the pixels in the image and assign new RGB values 
to get the segmented image.

Running the Application
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$ python segment.py bandwidth_value is_kernel_gaussian
[here is_kernel_guassian should be 0 if flat kernel is to be used otherwise it should be 1]

RESULTS (FLAT KERNEL) - #MEANS VERSUS BANDWIDTH
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IMG/BW			|   10   |   20   |   30   |   40   |   50   |   60   |   80   |	
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input1			|   93   |   73   |   66   |   62   |   61   |   60   |   61   |
input2			|   80   |   66   |   63   |   61   |   58   |   59   |   59   |
input3			|   95   |   75   |   65   |   57   |   56   |   55   |   52   |
input4			|   89   |   70   |   62   |   59   |   60   |   55   |   52   |
input5			|   87   |   68   |   63   |   60   |   61   |   62   |   59   |


RESULTS (GAUSSIAN KERNEL) - #MEANS VERSUS BANDWIDTH
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IMG/BW			|   10   |   20   |   30   |   40   |   50   |   60   |   80   |	
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input1			|   97   |   74   |   66   |   64   |   64   |   61   |   59   |
input2			|   80   |   69   |   61   |   61   |   60   |   59   |   57   |
input3			|  101   |   72   |   63   |   61   |   58   |   56   |   57   |
input4			|   95   |   70   |   65   |   61   |   61   |   59   |   55   |
input5			|   94   |   69   |   61   |   61   |   61   |   60   |   60   |

INFERENCES
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By plotting the number of converged means vs. Bandwidth for segmentation with flat kernel, we see 
that the elbow of most of the curves is located at bandwidth = 40. So, we say that this value of bandwidth 
should be used.
Similarly, in plots for segmentation with the gaussian kernel, we have the elbows located near bandwidth = 30.
So, we say that 30 is the good value of bandwidth.

If we keep on inceasing the bandwidth, we will lose useful information. This can be seen easily in segmentation 
result for the image 'input5.jpg', wherein the soldiers and trees all appear to be mixed up.  


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