deletions | additions       diff --git a/subsection_Clustering_Based_Methods_Clustering__.tex b/subsection_Clustering_Based_Methods_Clustering__.tex  index 1087911..6c0bd2b 100644  --- a/subsection_Clustering_Based_Methods_Clustering__.tex  +++ b/subsection_Clustering_Based_Methods_Clustering__.tex 
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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. The K-Means algorithm starts by randomly selecting $k$ pixels as the initial centroids for the clusters. The centroids represent the clusters and $k$ is a user defined parameter that sets the number of clusters to be formed. The rest of the algorithm is an iterative process and proceeds as follows: (Step 1) Label all theother  pixels to the cluster with the centroid nearest to them. This study used the Euclidean distance as their distance function. (Step 2) Compute for the new centroids of each cluster by getting the mean of all the pixels within the cluster. (Step 3) Repeat Steps 1 and 2 until the labels of the pixels do not change anymore.