GRID: GRID Resample by Information Distribution

This paper exploits a concise yet efficient initialization strategy to optimize grid sampling-based superpixel segmentation algorithms. Rather than straight distributing all initial seeds evenly, it adopts a context-aware approach to modify their positions and total number via a coarse-to-fine manner. Firstly, half the expected number of seeds are regularly sampled on the image grid, thereby creating a rough distribution of color information for all rectangular cells. A series of fission is then performed on cells that contain excessive color information recursively. In each cell, the local color uniformity is balanced by a dichotomy on one original seed, which generates two new seeds and settles them to spatially symmetrical sub-regions. Therefore, the local concentration of seeds is adaptive to the complexity of regional information. In addition, by calculating the amount of color via a summed area table (SAT), the informative regions can be located at a very low time cost. As a result, superpixels are produced from ideal original seeds with an exact number and exhibit better boundary adherence. Experiments demonstrate that the proposed strategy effectively promotes the performance of simple linear iterative clustering (SLIC) and its variants in terms of several quality measures.