deletions | additions       diff --git a/subsection_Identifying_the_Infected_Clusters__.tex b/subsection_Identifying_the_Infected_Clusters__.tex  index 0e694ed..5923a5e 100644  --- a/subsection_Identifying_the_Infected_Clusters__.tex  +++ b/subsection_Identifying_the_Infected_Clusters__.tex 
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\subsection{Identifying the Infected Clusters}  The function of the  K-Means algorithm isable  to cluster similar pixels together, thus separating together. By doing so, it is able to separate  the infected pixels from the healthy pixels. However, the algorithm is oblivious as to which clusters contain infected pixels and which clusters contain healthy pixels.  This information is necessary for the computation of the infestation level.  The task problem  now becomes changes from a segmentation problem to  a classification problem.  A classifier requires a set of features that represent the data points and discriminate between their classes. In this case, the data points are the clusters of pixels obtained from the K-Means algorithm. We observed that humans rely on color to distinguish the infected part of the fruit. Therefore, it is logical to choose color as the main feature for the classifier. We transformed the clusters into feature vectors to be used in training the classifier. The feature vector is composed of the centroids $a$ and $b$ (in L*a*b* color space computed from the K-Means algorithm), and the average values of red, green, and blue ($\mu_r$, $\mu_g$, $\mu_b$ in the RGB color space) taken over all the pixels within cluster.