deletions | additions       diff --git a/subsection_Identifying_the_Infected_Clusters__.tex b/subsection_Identifying_the_Infected_Clusters__.tex  index 8485629..0e694ed 100644  --- a/subsection_Identifying_the_Infected_Clusters__.tex  +++ b/subsection_Identifying_the_Infected_Clusters__.tex 
...
The task now becomes 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.