目前中文博客上介紹兩者區別的幾乎沒有，我就從外網搬運過來了，有兩處，供參考：
中文翻譯：
如果使用-n標誌，則將像素到給定超像素的光譜距離除以以前觀察到的到該超像素的最大光譜距離。這是所謂的SLIC0（SLIC零）方法的改編 。
每次迭代後，從分配給該超像素的所有像素確定到超像素的最大光譜距離。在下一次迭代中，更新超像素的像素分配，並在評估像素到超像素的潛在分配時，將像素到超像素的光譜距離除以當前超像素的最大光譜距離。
與Achanta等人相反。SLIC0的版本，即使使用-n標誌，i.superpixels.slic也會 考慮用戶選擇的緊密度值。
SLIC0意味着更多異構的超像素具有更大的最大光譜距離。對於給定的像素，與非均質超像素相比，異質性超像素的歸一化光譜距離將更小。這有利於更多的異構超像素，即使像素到同質超像素的未歸一化光譜距離小於到異構像素的光譜距離，也可以從更同質的超像素竊取像素。結果，異類超像素可能變得更大，甚至更加異類。當相鄰超像素的光譜均勻性差異較大時，且緊密度值較低時，此效果會變得更強 ，因爲光譜差異會變大。
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.
中文翻譯：
SLICO，SLIC的零參數版本
SLIC對圖像中的所有超像素使用相同的compactnes參數（由用戶選擇）。如果圖像在某些區域中是平滑的，而在其他區域中是高度紋理化的，則SLIC在平滑區域中生成平滑的規則大小的超像素，在紋理區域中生成高度不規則的超像素。因此，爲每個圖像選擇正確的參數變得很棘手。
SLICO完全解決了這個問題。用戶不再需要設置緊密度參數或嘗試使用不同的值。SLICO爲每個超像素自適應地選擇緊密度參數。這樣會在紋理區域和非紋理區域都生成規則形狀的超像素。改進幾乎不會影響計算效率– SLICO的速度與SLIC一樣快。
在下圖中，圖像的第一行顯示了所有超像素具有恆定壓縮因子的SLIC輸出，而圖像的下一行顯示了SLICO的輸出，SLICO的輸出針對每個超像素自適應地選擇了壓縮因子。
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.
參考：
https://grass.osgeo.org/grass76/manuals/addons/i.superpixels.slic.html
https://ivrl.epfl.ch/research-2/research-current/research-superpixels/