Demo.m shows a K-means segmentation demo
K-means clustering is one of the popular algorithms in clustering and segmentation. K-means segmentation treats each imgae pixel (with rgb values) as a feature point having a location in space. The basic K-means algorithm then arbitrarily locates, that number of cluster centers in multidimensional measurement space. Each point is then assigned to the cluster whose arbitrary mean vector is closest. The procedure continues until there is no significant change in the location of class mean vectors between successive iterations of the algorithms.
kindly requesting friends. could any any one explain this code clearly?i didn't understand the operation in that.
Hi, thank you.
Could you include a confusion matrix in the code? I would like to see the resutls and compare with different images.
what's the meangin of "NAN centers"
To compute the accuracy, you will need the ground truth for test images. By subtracting and computing the difference between each segment's pixel with groundtruth the error and accuracy can be computed.
Hai Alireza thank you for it..
can you explain how to get the accuracy or plotting of image segmentation..I really hope u can help me, thanks
Inspired by: K-means clustering