Convexity constrained efficient superpixel and supervoxel extraction

This paper presents an efficient superpixel (SP) and supervoxel (SV) extraction method that aims improvements over the state-of-the-art in terms of both accuracy and computational complexity. Segmentation performance is improved through convexity constrained distance utilization, whereas computational efficiency is achieved by replacing complete region processing by a boundary adaptation technique. Starting from the uniformly distributed, rectangular (cubical) equal size (volume) superpixels (supervoxels), region boundaries are iteratively adapted towards object edges. Adaptation is performed by assigning the boundary pixels to the most similar neighboring SPs (SVs). At each iteration, SP (SV) regions are updated; hence, progressively converging to compact pixel groups. Detailed experimental comparisons against the state-of-the-art competing methods validate the performance of the proposed technique considering both accuracy and speed. HighlightsAn iterative superpixel extraction method is proposed where only boundary pixels are updated for an efficient computation.Color term in the cost function enables strong adaptation on object boundaries and distance term enforces a geometry constrain.Extension of the method on spatio-temporal volume shows region adaptation in video footage.Superior or competing quantitative performance is observed in comparison with the state-of-the-art techniques.