Content deleted Content added
link malik |
|||
Line 36:
<math>nassoc(A, B) = \frac{w(A, A)}{w(A, V)} + \frac{w(B, B)}{w(B, V)}</math>
In the normalized cuts approach<ref>Jianbo Shi and [[Jitendra Malik]] (1997): "Normalized Cuts and Image Segmentation", IEEE Conference on Computer Vision and Pattern Recognition, pp 731-737 </ref>, for any cut <math>(S, \overline{S})</math> in <math>G</math>, <math>ncut(S, \overline{S})</math> measures the similarity between different parts, and <math>nassoc(S, \overline{S})</math> measures the total similarity of vertices in the same part.
Since <math>ncut(S, \overline{S}) = 2 - nassoc(S, \overline{S})</math>, a cut <math>(S^{*}, {\overline{S}}^{*})</math> that minimizes <math>ncut(S, \overline{S})</math> also maximizes <math>nassoc(S, \overline{S})</math>.
| |||