  Benefiting from its high efficiency and simplicity, Simple Linear Iterative
Clustering (SLIC) remains one of the most popular over-segmentation tools.
However, due to explicit enforcement of spatial similarity for region
continuity, the boundary adaptation of SLIC is sub-optimal. It also has
drawbacks on convergence rate as a result of both the fixed search region and
separately doing the assignment step and the update step. In this paper, we
propose an alternative approach to fix the inherent limitations of SLIC. In our
approach, each pixel actively searches its corresponding segment under the help
of its neighboring pixels, which naturally enables region coherence without
being harmful to boundary adaptation. We also jointly perform the assignment
and update steps, allowing high convergence rate. Extensive evaluations on
Berkeley segmentation benchmark verify that our method outperforms competitive
methods under various evaluation metrics. It also has the lowest time cost
among existing methods (approximately 30fps for a 481x321 image on a single CPU
core).
