Vozel B., Chehdi K., Klaine L. Noise identification and estimation of its statistical parameters by using unsupervised variational classification // Proceedings of ICASSP, Vol. II, pp. 841-844, 2006
Popov A., Pogrebnyak O., Brashevan A. Unsupervised Remote Sensing Data Classification Using Multimodal Statistical Model // Proc. of the First Intern. Conf. on Industrial Informatics, Mexico, 2007
Vozel B., Chehdi K., Klaine L. Noise identification and estimation of its statistical parameters by using unsupervised variational classification // Proceedings of ICASSP, Vol. II, pp. 841-844, 2006
Popov A., Pogrebnyak O., Brashevan A. Unsupervised Remote Sensing Data Classification Using Multimodal Statistical Model // Proc. of the First Intern. Conf. on Industrial Informatics, Mexico, 2007
Hello, you can see the Mean Shift Algorithm, this alternative was proposed by Dorin Comaniciu and have very much applications in computer vision. It's a very adaptable alternative. Consider this links, they are some papers for clustering using Mean Shift.
You can use MeanShift. It is OK if you want to cluster the image without prior information about the number of clustering. You need to choose the kernel and the bandwidth parameters. you will find a lot of implanetation for meanshift in matlab and C++.
Also, you can find SLIC Superpixels, It works properly. In addition, NCUTS segmentation.