Segmentation-only research focuses on the actual image segmentation algorithms -- where the output of a segmentation algorithm is a partition of a 2D image into contiguous regions. Algorithms such as mean-shift, normalized cuts, as well as 100s of probabilistic graphical models can be used produce such segmentations. The Berkeley group (in an attempt to salvage "mid-level" vision) has been working diligently on boundary detection and image segmentation for over a decade.
Recognition-only research generally focuses on new learning techniques or building systems to perform well on detection/classification benchmarks. The sliding window approach coupled with bag-of-words models has dominated vision and is the unofficial method of choice.
It is easy to relax the bag-of-words model, so let's focus on rectangles for a second. If we do not use segmentation, the world of objects will have to conform to sliding rectangles and image parsing will inevitably look like this:
It has been argued that segmentation is required to move beyond the world of rectangular windows if we are to successfully break up images into their constituent objects. While some objects can be neatly approximated by a rectangle in the 2D image plane, to explain away an arbitrary image free-form regions must be used. I have argued this point extensively in my BMVC 2007 paper, and the interesting result was that multiple segmentations must by used if we want to produce reasonable segments. Sadly, segmentation is generally not good enough by itself to produce object-corresponding regions.
The question of how to use segmentation algorithms for recognition is still open. If segmentation could tessellate an image into "good" regions in one-shot then the goal of recognition is to simply label these regions and life becomes simple. This is unfortunately far from reality. While blobs of homogeneous appearance often correspond to things like sky, grass, and road, many objects do not pop out as a single segment. I have proposed using a soup of such segments that come from different algorithms being ran with different parameters (and even merging pairs and triplets of such segments!) but this produces a large number of regions and thus making the recognition task harder.
Using a soup of segments, a small fraction of the regions might be of high quality; however, recognition now has to throw away 1000s of misleading segments. Abhinav Gupta, a new addition to CMU vision community, has pointed out that if we want to model context between segments (and for object-object relationships this means a quadratic dependence on the number of segments), using a large soup of segments in simply not tractable. Either the number of segments or the number of context interactions has to be reduced in this case, but non-quadratic object-object context models are an open question.
In conclusion, the representation used by segmentation (that of free-form regions) is superior to sliding window approaches which utilize rectangular windows. However, off-the-shelf segmentation algorithms are still lacking with respect to their ability to generate such regions. Why should an algorithm that doesn't know anything about objects be able to segment out objects? I suspect that in the upcoming years we will see a flurry of learning-based segmenters that provide a blend of recognition and bottom-up grouping, and I envision such algorithms to be used a strictly non-feedforward way.