deletions | additions       diff --git a/subsection_Texton_Dictionary_Generation_label__.tex b/subsection_Texton_Dictionary_Generation_label__.tex  index 65ead6e..70fe678 100644  --- a/subsection_Texton_Dictionary_Generation_label__.tex  +++ b/subsection_Texton_Dictionary_Generation_label__.tex 
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\subsection{Texton Dictionary Generation}  \label{sec:text-dict-gener}  The proposed approach requires a dictionary of textons, such that the histograms can be determined. Different maps and environmental settings require different textons. While we set the number of textons to 20 for all maps, this parameter is also map-dependent, and ideally, if time allows can be adapted to the given map.  For learning a suitable dictionary for a given an  environment, image patches were clustered. The resulting cluster centers---the prototypes of the clustering result---are the textons~\cite{varma2003texture}.Different situations require different textons and a different number of them. The choice of these parameters is map-dependent, and we set it to 20 textons for all maps.  The clustering was performed using a competitive learning scheme with a winner-take-all strategy. In the beginning, the textons are dictionary is  initialized with 20 random patches from the first image. image, which form the first guess for cluster centers.  Then, each patch is new images patches are extracted and  compared to each texton in the tentative dictionary  using the Euclidean distance. The most similar texton to the current patch is declared as the ``winner". The This  texton is then adapted to be more similar to the current patch, by calculating the difference in pixel values between the texton and the current images patch, and  updating it the texton  with a learning rate of $\alpha = 0.02$. The first 100 images of each dataset were used to generate the dictionary. For each image, 1000 randomly selected image patches of size $w \times h = 6 \times 6$ were extracted, yielding $100,000$ image patches in total that were clustered. An example of a learned dictionary can be found in Figure~\ref{fig:dictionary}.