LASP: Local adaptive super-pixels

In this study, a novel gradient ascent approach is proposed for super-pixel extraction in which spectral statistics and super-pixel geometry are utilized to obtain an optimal Bayesian classifier for pixel to super-pixel label assignment. Utilization of the spectral variances and super-pixel areas reduces the dependency on user selected global parameters, while increasing robustness and adaptability. Proposed Local Adaptive Super-Pixels (LASP) approach exploits hexagonal tiling, while achieving some refinement during initialization in order to improve computation time and accuracy. The experiments conducted on Berkeley segmentation database show that LASP outperforms the existing methods in terms of boundary recall and computation time. Moreover, the proposed method provides lower bleeding error performance compared to the existing gradient ascent techniques.