目前中文博客上介绍两者区别的几乎没有，我就从外网搬运过来了，有两处，供参考：
中文翻译：
如果使用-n标志，则将像素到给定超像素的光谱距离除以以前观察到的到该超像素的最大光谱距离。这是所谓的SLIC0（SLIC零）方法的改编 。
每次迭代后，从分配给该超像素的所有像素确定到超像素的最大光谱距离。在下一次迭代中，更新超像素的像素分配，并在评估像素到超像素的潜在分配时，将像素到超像素的光谱距离除以当前超像素的最大光谱距离。
与Achanta等人相反。SLIC0的版本，即使使用-n标志，i.superpixels.slic也会 考虑用户选择的紧密度值。
SLIC0意味着更多异构的超像素具有更大的最大光谱距离。对于给定的像素，与非均质超像素相比，异质性超像素的归一化光谱距离将更小。这有利于更多的异构超像素，即使像素到同质超像素的未归一化光谱距离小于到异构像素的光谱距离，也可以从更同质的超像素窃取像素。结果，异类超像素可能变得更大，甚至更加异类。当相邻超像素的光谱均匀性差异较大时，且紧密度值较低时，此效果会变得更强 ，因为光谱差异会变大。
英文原文：
Normalization of spectral distances (SLIC0)
If the -n flag is used, the spectral distance of a pixel to a given superpixel is divided by the maximum previously observed spectral distance to that superpixel. This is an adaptation of the so-called SLIC0 (SLIC zero) method.
After each iteration, the largest spectral distance to a superpixel is determined from all pixels assigned to that superpixel. In the next iteration, pixel assignment to superpixels is updated and spectral distances of pixels to superpixels are divided by the largest spectral distance of the current superpixel when evaluating a potential assignment of a pixel to a superpixel.
Contrary to the Achanta et al. version of SLIC0, i.superpixels.slic takes into account the compactness value chosen by the user even when the -n flag is used.
SLIC0 implies that more heterogeneous superpixels have a larger maximum spectral distance. For a given pixel, the normalized spectral distance will be smaller for a more heterogeneous superpixel than for a more homogeneous superpixel. This favours more heterogeneous superpixels which can steal pixels from more homogeneous superpixels even if the not normalized spectral distance of a pixel to a homogeneous superpixel is smaller than to a heterogeneous pixel. As a consequence, heterogeneous superpixels can become larger and and even more heterogeneous. This effect becomes stronger with larger differences in the spectral homogeneity of neighboring superpixels, and with a lower compactness value, as spectral difference then gets a bigger weight.
中文翻译：
SLICO，SLIC的零参数版本
SLIC对图像中的所有超像素使用相同的compactnes参数（由用户选择）。如果图像在某些区域中是平滑的，而在其他区域中是高度纹理化的，则SLIC在平滑区域中生成平滑的规则大小的超像素，在纹理区域中生成高度不规则的超像素。因此，为每个图像选择正确的参数变得很棘手。
SLICO完全解决了这个问题。用户不再需要设置紧密度参数或尝试使用不同的值。SLICO为每个超像素自适应地选择紧密度参数。这样会在纹理区域和非纹理区域都生成规则形状的超像素。改进几乎不会影响计算效率– SLICO的速度与SLIC一样快。
在下图中，图像的第一行显示了所有超像素具有恒定压缩因子的SLIC输出，而图像的下一行显示了SLICO的输出，SLICO的输出针对每个超像素自适应地选择了压缩因子。
英文原文：
How is SLICO different from SLIC?
SLIC uses the same compactnes parameter (chosen by user) for all superpixels in the image. If the image is smooth in certain regions but highly textured in others, SLIC produces smooth regular-sized superpixels in the smooth regions and highly irregular superpixels in the textured regions. So, it become tricky choosing the right parameter for each image.
SLICO does away with this problem completely. The user no longer has to set the compactness parameter or try different values of it. SLICO adaptively chooses the compactness parameter for each superpixel differently. This generates regular shaped superpixels in both textured and non textured regions alike. The improvement comes with hardly any compromise on the computational efficiency – SLICO continues to be as fast as SLIC.
In the figure below, the top row of images shows SLIC output with a constant compactness factor for all superpixels, while the bottom row of images shows the ouput of SLICO, which chooses the compactness factor adaptively for each superpixel.
参考：
https://grass.osgeo.org/grass76/manuals/addons/i.superpixels.slic.html
https://ivrl.epfl.ch/research-2/research-current/research-superpixels/
