Felzenszwalb, P. F. & Huttenlocher, D. P. Efficient graph-based image segmentation International Journal of Computer Vision, 2004, 59, 167-181

this is a method for over segmentation but object segmentation result could not be produce.

you can also refer to this article

Carreira, J. & Sminchisescu, C. Constrained parametric min-cuts for automatic object segmentation Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 2010, 3241-3248

this method is similar to Graphcut, but EM is not needed in this method. The comparision between neighbour region is achieved through a simple way instead of EM.

Mean shift segmentation is one of the best method.  you may try segmentation using Euler graphs too.

Dear Marcus

I Send you some useful Papers as a PDF  :

Some papers could be downloaded by searching it with Researhgate search engine

I suggest to follow the authors of these papers and then messaging him asking more help about Graph Cut

Also, a useful full source code written with C, C++ and Matlab were attached

Please do not hesitate in any inquiry

Thanks

Dear Marcus

In this link you find several code of efficient segmentation methods.

https://sites.google.com/site/uqchang/home/cv-code

Segmentation methods depends on what what you want to segment from image.

load your image to see what algorithm fit with your request. some algorithms need to transform image to fit with it or if you do not how to transfer your image you need to know how the algorithm work so you can change in its rule to give you a perfect result after comparing the segment result with the ground truth.

best regards. 

Dear Marcus,

What kind of image do you want to segment?

Do you have prior knowledge on the segmented shape or content?

What are your goals in terms of sensitivity/ error?

Cheers

Renaud

i thank the most efficient image segmentation methods depend on your application and the type of images 

It is totally dependent on the kind of images which you are handle and the "object" that you want to segment ...

Segmentation using geodesic distances proved to be very efficient because it does not involve any energy minimization step. When coupled with superpixels it becomes even more efficient [e.g. hundreds of segmentations  per second in the "geodesic object proposal method"].

You can implement it on your own with few lines of code, following the description of the Dijkstra's algorithm. You can contact me for further details.

http://vladlen.info/publications/geodesic-object-proposals/

Different segmentation methods are available for example

1. color  segmentation

2.water set segmentation

3.Histogram-based methods

4.Multi-scale segmentation

5.Edge detection

Dear Chen:

There are more than 40 segmentation methods. However, all depends on researcher choice, I usually use the Otsu method if the image shows a bi-modal structure but one of the most recommended is Mathematical Morphology developed at INRIA by Levy-Vehel et al.

Cheers Dr. Millan

You can observe different segmentation algorithms in the following publications. They are useful for practical applications: 

Article Automatic building extraction in dense urban areas through G...

Conference Paper Automatic Extraction of Buildings in a Dense Urban Area from...

Conference Paper Automatic Extraction of Buildings in Urban Areas from Very H...

Conference Paper AN AUTOMATIC REGION-BASED SEGMENTATION ALGORITHM FOR BUILDIN...