image segmentation lecture notes

The previous segmentations were done by the local variation (LV) algorithm , spectral min-cut (SMC), human (H) edgeaugmented mean-shift (ED) , and normalized cut (NC) [13, 5].
• The quality of the segmentation depends on the image. Smoothly
shaded surfaces with clear gray-level steps between different surfaces are ideal for the above algorithms.
• Humans probably use object recognition in conjunction with segmentation, although the machine algorithms exhibited above do
• For relatively simple images it is plausible that machine segmentations, such as those shown on p.2, are useful for several visual
tasks, including object recognition.
• For more complex images (pp. 5, 6), the machine segmentations
provide a less reliable indicator for surface boundaries, and their
utility for subsequent processing becomes questionable.
• While many segmentation algorithms work well with simple examples, they will all break down given examples with enough clutter and camouflage. The assessment of segmentation algorithms
therefore needs to be done on standardized datasets

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