Most common modern day Image Segmentation techniques
I do not know about many segmentation techniques, but I have been dealing with structures that offer a "choice" of segmentation pieces that can be further examined to produce a satisfying segmentation.

Hopefully somebody else can write about some different state-of-the-art segmentation method that I don't know much about.

A small introduction as to why it is nice to have choices, or levels, of segmentation for a different image: segmentation is an ill defined problem. The ground truth does not exist: the desired results always depend on the user requirements and specifications. A nice quote:


  Even for a fixed image, there may be more than one "best" segmentation because the criteria defining the quality of a segmentation are application dependent. This motivated us to focus our research on image-partitioning techniques delivering "puzzle pieces" that can be used (...) to generate a segmentation satisfying specific user requirements


(P. Soille: Constrained Connectivity for Hierarchical Image Partitioning and Simplification (2008))

There are hierarchical structures, hierarchical image decompositions that propose image partitions with varying complexities. These structures are most simply represented as tree structures, where each node represents a region in the image. The idea with the structures:


the leaves of the structure are regions of a fine partition, or over-segmentation of an image (e.g. pixels, flat zones - connected regions of same intensity, watershed)
the links in the tree represent merging or union of (neighboring) regions and forming a more complex region, and are constructed in a way most likely to produce regions corresponding to objects (hopefully :) )
the complexity of the regions increases along each branch from nodes towards the root of the tree
every level of the tree (also, every cut) is a partitioning/segmentation of an image (coarser the closer to the root it is)
the root of the tree covers the whole image domain


The segmentation than consists of examining the regions and their proposed unions, to determine the regions in the tree or the cut of the tree corresponding to the required precision, or some known properties about object of interest, or other pre-defined user specifications.

Trees (that is, hierarchical image decompositions) with such properties are:


$\alpha$-trees
$(\omega)$-trees
Binary partition trees (aimed at segmentation, and object detection)


In addition to links to papers already provided, some more specific and less specific current papers on these kind of segmentation techniques:


Cousty, J., Najman, L.: Incremental Algorithm for Hierarchical Minimum Spanning Forests and Saliency of Watershed Cuts (2011)
Soille, P., Grazzini, J.: Constrained Connectivity and Transition Regions (2009)


(more practical):


Serna, A., Marcotegui, B.: Attribute Controlled Reconstruction and Adaptive Mathematical Morphology (2013)
Merciol, F., Lefèvre, S.: Fast image and video segmentation based on alpha-tree multiscale representation (2012)