I agree with Dave. The authors assume that object shape is consistent, the variability of color/texture within a single instance of an object is limited, and that objects are moderately large (about 15-30% of the image). Even though the objects of interest aren't in the exact center of the image, foreground/background separation isn't very difficult. Other limitations are: the authors flip asymmetric objects to face a consistent direction and images contain only one object of interest.

On page 7 of the paper the authors display the 88.6% segmentation accuracy for LOCUS with no class model applied to horses (they simply learn a mask and color model for each image so as to minimize the visual entropy within each part of the scene). I think that if they want to show the strengths of an unsupervised learning technique, then they need to analyze a more difficult data set where segmentation accuracy without using class information is low (about 50%).

On a more positive note, this paper does a good job at demonstrating the recent 'being bayesian about segmentation' trend. The authors have a generative model for images of an object class and by being bayesian they segment images automatically using "all images together."

In summary, I'm not impressed by the results; however, I like the Bayesian approach of LOCUS.