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In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels. Our main emphasis is
on speed and accuracy. We build on [31] to define the problem as a boundary and topology preserving Markov random
field. We propose a coarse to fine optimization technique
that speeds up inference in terms of the number of updates
by an order of magnitude. Our approach is shown to outperform [31] while employing a single iteration. We evaluate
and compare our approach to state-of-the-art superpixel algorithms on the BSD and KITTI benchmarks. Our approach
significantly outperforms the baselines in the segmentation
metrics and achieves the lowest error on the stereo task.
2016-01-21
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