deletions | additions       diff --git a/subsection_Clustering_Based_Methods_Clustering__.tex b/subsection_Clustering_Based_Methods_Clustering__.tex  index b7539b1..36acd61 100644  --- a/subsection_Clustering_Based_Methods_Clustering__.tex  +++ b/subsection_Clustering_Based_Methods_Clustering__.tex 
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Clustering is the task of grouping objects into different groups, referred to as clusters. Different clustering methods have their own way of choosing which objects belong to which clusters. One commonality is that similar objects tend to be in the same cluster. A distance function determines what is considered similar and what is not. The most common distance function is the Euclidean Distance, which uses a straight line between two points in Euclidean space as a basis for the distance.   Dubey et al. \cite{dubey2013infected} proposed using K-Means clustering to segment the infected part of an apple. They converted the image first from the RGB color space to the L*a*b* color space. Commission Internationale d’Eclairage (CIE) designed the L*a*b* color space to match how humans perceive differences in color and luminance \cite{szeliski2010computer}, thus making it a good color space for computing distances. It is composed of a luminosity or lightness dimension (L*) and two chromaticity or color dimension (a*b*). Isolating the color information to two dimensions (in L*a*b*) makes it computationally more efficient than having the color information spread to three dimensions (in RGB) \cite{dubey2013infected}. The pixels are then clustered in the a*b* space using the K-Means algorithm. It starts by randomly selecting $k$ pixels as the initial centers of the clusters. $k$ is a user defined parameter that sets the number of clusters to be formed.   This study used the Euclidean distance function.