Agglomerative Clustering Stopping Criteria I am trying to implement section 3.4 of paper Predicting Important Objects for Egocentric Video Summarization where they have created a distance matrix of frame histograms.

In short, let say Ω is mean of distances between all frames,DVis distance matrix.

I didn't understand what is meant by this: 


  We next perform complete-link agglomerative clustering
  with DV , grouping frames until the smallest maximum interframe
  distance is larger than two standard deviations beyond
  Ω


Can this be achieved by setting the cutoff value to 2Ω in Matlab's clusterdata function ?
Clearly, 2 standard deviations beyond Omega is not the same as twice the mean.

Apparently, their process is this:


compute the distance matrix
compute the mean
compute the standard deviation
compute hierarchical clustering with maximum linkage
cut the tree at mu+2*sigma


Because complete linkage is in O(n^3), this approach will not scale to longer videos or higher frame rates.